Methods for identifying and using organ-specific proteins in blood

ABSTRACT

The present invention relates generally to methods for identifying organ-specific secreted proteins and for identifying organ-specific molecular blood fingerprints therefrom. As such, the present invention provides compositions comprising such proteins, detection reagents for detecting such proteins, and panels, and arrays for determining organ-specific molecular blood fingerprints.

STATEMENT OF GOVERNMENT INTEREST

This invention was made with government support under Grant Nos. P50 CA097186 and P01 CA085857 awarded by the National Cancer Institute. The government may have certain rights in this invention.

STATEMENT REGARDING SEQUENCE LISTING SUBMITTED ON CD-ROM

The Sequence Listing associated with this application is provided on CD-ROM in lieu of a paper copy, and is hereby incorporated by reference into the specification. Three CD-ROMs are provided, containing identical copies of the sequence listing: CD-ROM No. 1 is labeled COPY 1, contains the file 401.app.txt which is 3.31 MB and created on Jan. 27, 2006; CD-ROM No. 2 is labeled COPY 2, contains the file 401.app.txt with is 3.31 MB and created on Jan. 27, 2006; CD-ROM No. 3 is labeled CRF (Computer Readable Form), contains the file 401.app.txt which is 3.31 KB and created on Jan. 27, 2006.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to methods for identifying organ-specific proteins that are secreted into the blood. The invention further relates to methods of diagnosis and methods of use of such proteins.

2. Description of the Related Art

The ability to detect the onset of disease very early has been a longtime goal of the diagnostic field. Early detection will in most cases permit the disease to be effectively dealt with. For example, with most cancers, early detection would permit a patient to be cured by conventional therapies (chemotherapy, radiation, surgery). Hence early diagnosis is the cornerstone of dealing effectively with many diseases.

Differentially expressed proteins, particularly proteins found in blood, may serve as biological markers that can be measured for diagnostic (or therapeutic) purposes. Different approaches for measuring blood proteins have been used with varying degrees of success. In particular, two-dimensional (2-DE) gel electrophoresis is widely used for analysis of proteomic patterns in blood and other tissues. However, several limitations restrict its utility in diagnostic proteomics. First, because (2-DE) gels are limited to spatial resolution, it is difficult to resolve large numbers of proteins such as are expressed in the average cell (1,000 to 10,000 proteins) or even worse—blood. High abundance proteins can distort carrier ampholyte gradients in capillary isoelectric focusing electrophoresis (CGE) and result in crowding in the gel matrix of size sieving electrophoretic methods (e.g., the second dimension of (2-DE) gel electrophoresis and CGE), thus causing irreproducibility in the spatial pattern of resolved proteins (see e.g., Corthals, G. L., et al. Electrophoresis, 18:317 (1997). Lopez, M. F., and W. F. Patton, Electrophoresis, 18:338 (1997)). Note, for example, that albumen constitutes about 51% of the blood protein. Indeed, 22 proteins constitute about 99% of the blood protein and most of these will not be useful diagnostic markers—those will be present in the 1% of the remaining proteins that are often hidden by the abundant proteins. High abundance proteins can also precipitate in a gel and cause streaking of fractionated proteins (Corthals, G. L., et al., supra). Variations in the crosslinking density and electric field strength in cast gels can further distort the spatial pattern of resolved proteins. Another problem is the inability to resolve low abundance proteins neighboring high abundance proteins in a gel because of the high staining background and limited dynamic range of gel staining and imaging techniques. Limitations with staining also make it difficult to obtain reproducible and quantifiable protein concentration values, with average standard variations in relative protein abundance between replicate (2-DE) gels reported to be 20% and as high as 45% (Anderson, L. and J. Seilhamer, Electrophoresis, 18:533 (1997)). For example, investigators were only able to match 62% of the spots formed on 3-7 gels run under similar conditions (Lopez, M. F., and W. F. Patton, supra; see also Blomber, A., et al., Electrophoresis, 16:1935 (1995) and Corbett, J. M., et al., Electrophoresis, 15:1205 (1994)). Additionally, many proteins are not soluble in buffers compatible with acrylamide gels, or fail to enter the gel efficiently because of their high molecular weight (see e.g., Ramsby, M., et al., Electrophoresis, 15:265 (1994)).

Thus, a major stumbling block in the diagnostic proteomic analysis of the blood is the high degree of complexity of the blood proteome. Another major challenge is the large dynamic range across which proteins are expressed—about 10e¹⁰. This means that one protein may be present at one copy in a given volume, whereas another may be present at 10e¹⁰ copies. Additionally, pattern analysis using techniques such as 2-DGE and other similar techniques has been problematic primarily as a result of the irreproducibility of the gel patterns, inability to detect very low abundance proteins, difficulty in quantitating the individual spots (e.g., proteins) that make up a complex proteomic pattern and the inability to identify the individual proteins that constitute the complex pattern. Further, the ability to extend these techniques to easy, consistent, and high throughput diagnostic assays has been extremely limited. Thus, there is a need in the art to provide such diagnostic assays. The present invention provides for methods and assays that fulfill these and other needs.

BRIEF SUMMARY OF THE INVENTION

One aspect of the invention provides a method for identifying organ-specific proteins secreted into the blood comprising, generating a signature sequence from transcripts from a sample from a specific organ; identifying transcripts that are specifically expressed in the organ; identifying from the transcripts in (b) those transcripts that encode secreted proteins; and thereby identifying organ-specific proteins secreted into the blood.

Another aspect of the invention provides a method for identifying organ-specific proteins secreted into the blood comprising, generating a signature sequence from substantially all transcripts from a sample from a specific organ; comparing the signature sequences to a database of known sequences to determine the identity of the transcript; comparing the identified transcripts to transcripts expressed in other organs; removing any transcripts that are substantially expressed in other organs; identifying computationally from the remaining transcripts those that encode a signal peptide; confirming the presence of the secreted proteins in a blood sample; and thereby identifying organ-specific proteins secreted into the blood.

In a further aspect, the present invention provides a method for diagnosing a biological condition in a subject comprising measuring the level of a plurality of organ-specific proteins in the blood of the subject, wherein the plurality of organ-specific proteins are secreted from the same organ and wherein the levels of the plurality of organ-specific proteins together provide a diagnostic fingerprint for the biological condition in the subject. In one embodiment of the method, the level of the plurality of organ-specific proteins is measured using any one or more methods, such as mass spectrometry, an immunoassay such as an ELISA, Western blot, microfluidics/nanotechnology sensors, and aptamer capture assay. In this regard, an aptamer may be used in a similar manner to an antibody in a variety of appropriate binding assays known to the skilled artisan and described herein. In certain embodiments, the plurality of organ-specific proteins is measured using tandem mass spectrometry or other spectrometry-based techniques. In one embodiment, the plurality of organ-specific proteins comprises from at least about 1 or 2 organ-specific proteins to about 100, 150, 160, 170, 180, 190, 200, or more organ-specific proteins. In this regard, the plurality of organ-specific proteins may comprise at least 2, 3, 4, 5, 6, 7 8, 9, 10, or more organ-specific proteins. The plurality of organ-specific proteins may comprise about 10 or 20 organ-specific proteins. In one embodiment, the organ-specific proteins comprise prostate-specific proteins. In one embodiment, the prostate-specific proteins are selected from the proteins listed in Table 4 and Table 5. In other embodiments, the organ-specific proteins may be from any organ, such as liver, kidney, breast, ovary, etc. In one embodiment, the method is used to diagnose any of a variety of biological conditions, such as cancer. In this regard, the cancer can be any cancer, such as, but not limited to, brain cancer, bladder cancer, prostate cancer, ovarian cancer, breast cancer, liver cancer, lung cancer, pancreatic cancer, kidney cancer, and colon cancer. In a further embodiment, the biological condition is any one or combination of the following: cardiovascular disease, metabolic disease, infectious disease, genetic disease, autoimmune disease, and immune-related disease.

Another aspect of the present invention provides a method for determining the presence or absence of disease in a subject comprising, detecting a level of each of a plurality of organ-specific proteins in a blood sample from the subject, wherein the plurality of organ-specific proteins are secreted from the same organ; comparing the level of each of the plurality of organ-specific proteins in the blood sample from the subject to a level of the plurality of organ-specific proteins in a normal control sample of blood; wherein an altered level of one or more of the plurality of organ-specific proteins in the blood is indicative of the presence or absence of disease. As would be readily appreciated by the skilled artisan, an altered level can mean an increase in the level or a decrease in the level. In this regard, the skilled artisan would readily appreciate that a variety of statistical tests can be used to determine if an altered level is significant. The Z-test (Man, M. Z., et al., Bioinformatics, 16: 953-959, 2000) or other appropriate statistical tests can be used to calculate P values for comparison of protein expression levels. In certain embodiments, the level of each of the plurality of organ-specific proteins in the blood sample from the subject is compared to a previously determined normal control level of each of the plurality of organ-specific proteins taking into account standard deviation. In one embodiment, the level of each of the plurality of organ-specific proteins is detected using any one or more of a variety of methods, such as, but not limited to mass spectrometry, and immunoassays. In certain embodiments, the level of each of the plurality of organ-specific proteins is measured using mass spectrometry (e.g., tandem mass spectrometry) or an immunoassay such as an ELISA. In an additional embodiment, the level of each of the plurality of organ-specific proteins is measured using an antibody array.

A further aspect of the present invention provides a method for detecting perturbation of a normal biological state comprising, contacting a blood sample with a plurality of detection reagents each specific for an organ-specific protein secreted into blood, wherein each organ-specific protein is secreted from the same organ; measuring the amount of the organ-specific protein detected in the blood sample by each detection reagent, comparing the amount of the organ-specific protein detected in the blood sample by each detection reagent to a predetermined control amount for each organ-specific protein; wherein a statistically significant altered level in one or more of the organ-specific proteins indicates a perturbation in the normal biological state. Thus, in one embodiment, the predetermined control amount is determined from one or more normal blood samples. The skilled artisan would readily appreciate that a variety of statistical tests can be used to determine if an altered level of a given protein is significant. The Z-test (Man, M. Z., et al., Bioinformatics, 16: 953-959, 2000) or other appropriate statistical tests can be used to calculate P values for comparison of protein expression levels. In certain embodiments, the level of each of the plurality of organ-specific proteins in the blood sample from the subject is compared to a previously determined normal control level of each of the plurality of organ-specific proteins taking into account standard deviation (see e.g., U.S. Patent Application No. 20020095259). In an additional embodiment the plurality of detection reagents comprises from at least about 2 detection reagents to about 100, 150, 160, 170, 180, 190, 200, or more detection reagents. In a further embodiment, the plurality of detection reagents comprises about 5, 10 or about 20 detection reagents. In one embodiment, the organ-specific proteins comprise prostate-specific proteins, liver-specific proteins, or breast-specific proteins. In this regard, the organ-specific proteins can be from any organ, tissue, cell, or system as described further herein.

A further aspect of the present invention provides a diagnostic panel for determining the presence or absence of disease in a subject comprising, a plurality of detection reagents each specific for detecting one of a plurality of organ-specific proteins present in a blood sample; wherein the organ-specific proteins are secreted from the same organ and wherein detection of the plurality of organ-specific proteins with the plurality of detection reagents results in a fingerprint indicative of the presence or absence of disease in the subject. As noted elsewhere herein, the term “subject” is intended to include humans Thus, as further described herein, the organ-specific molecular blood fingerprint is unique for a given disease and further for a given stage of the disease and thus is a powerful diagnostic indicator. In one embodiment, the detection reagents comprise antibodies or antigen-binding fragments thereof. In a further embodiment, the antibodies are monoclonal antibodies, or antigen-binding fragments thereof. In one embodiment, the panel comprises one or more detection reagents. In yet a further embodiment, the plurality of detection reagents comprises from at least about 1 detection reagent to about 100, 150, 160, 170, 180, 190, 200 or more detection reagents. In yet a further embodiment, the plurality of detection reagents comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 detection reagents. In certain embodiments, the plurality of detection reagents comprises about 5, 10, or 20 detection reagents. In an additional embodiment, the organ-specific proteins comprise prostate-specific, liver-specific, or breast-specific proteins. As would be recognized by the skilled artisan upon reading the present disclosure, the organ-specific protein may be derived from any organ, tissue, cell, as described further herein. In a further embodiment, the panel is used for determining the presence or absence of a cancer. In this regard, the panel can be used to determine the presence or absence of any cancer, including but not limited to any one or more of prostate cancer, ovarian cancer, breast cancer, liver cancer, lung cancer, pancreatic cancer, kidney cancer, and colon cancer. In an additional embodiment, the panel can be used to determine the presence or absence of any disease including but not limited to the following diseases: cardiovascular disease, metabolic disease, infectious disease, genetic disease, autoimmune disease, immune-related disease, neurological disease and cancer.

An additional aspect of the present invention provides an assay device comprising a panel of detection reagents wherein each detection reagent in the panel, with the exception of a negative and positive control, is capable of specific interaction with one of a plurality of organ-specific proteins secreted into the blood, wherein the plurality of organ-specific proteins are secreted from the same organ and wherein the pattern of interaction between the detection reagents and the organ-specific proteins present in a blood sample is indicative of a biological condition. In certain embodiments, the pattern of interaction is the combination of, a snapshot of sorts, of the different quantitative levels of the organ-specific proteins detected. Thus, in certain embodiments, the pattern of interaction is a set of numbers, each number corresponding to a level of a particular organ-specific protein. This set of numbers and the specific organ-specific proteins that they correspond to together make up the pattern of interaction (e.g., fingerprint) that defines a biological condition.

A further aspect of the present invention provides a method for diagnosing a biological condition in a subject comprising measuring the level of a plurality of organ-specific proteins in the blood of the subject, wherein the organ-specific proteins are secreted from the same organ or specific to the same organ and wherein the levels of the plurality of organ-specific proteins together provide a fingerprint for the biological condition in the subject; thereby diagnosing the biological condition. In one embodiment, a statistically significant altered level in one or more of the organ-specific proteins as compared to a predetermined normal level classifies the subject as having a perturbation from the normal biological state. In this regard, identifying altered levels in one or more of the organ-specific proteins as compared to predetermined normal levels can be used for classifying subjects by disease and disease stage or generally as having a perturbation from the normal biological state. In a further embodiment, the fingerprint is measured in the blood, serum or plasma of the subject. In certain embodiments, the plurality of organ-specific proteins comprises at least 2 or more organ-specific proteins. In this regard, the plurality of organ-specific comprises about 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 organ-specific proteins. In certain embodiments, the biological condition affects the prostate and wherein the organ-specific proteins are prostate-specific proteins. In a further embodiment, the biological condition affects the breast and wherein the organ-specific proteins are breast-specific proteins. In yet a further embodiment, the biological condition comprises a cancer. In this regard, a cancer may include, but is not limited to, prostate cancer, ovarian cancer, breast cancer, liver cancer, lung cancer, pancreatic cancer, kidney cancer, or colon cancer. In another embodiment, the biological condition may include but is not limited to cardiovascular disease, metabolic disease, infectious disease, genetic disease, autoimmune disease, immune-related disease, neurological disease and cancer.

Another aspect of the invention provides a method for diagnosing a biological condition in a subject comprising measuring the level of one or more organ-specific proteins in the blood of the subject, wherein the organ-specific proteins are secreted from the same organ and wherein the levels of the one or more organ-specific proteins together provide a fingerprint for the biological condition in the subject; thereby diagnosing the biological condition.

A further aspect of the invention provides a method for determining the presence or absence of disease in a subject comprising, a) detecting the level of each of a plurality of organ-specific proteins in a blood sample from the subject, wherein the plurality of organ-specific proteins are secreted from the same organ; b) comparing said level of each of the plurality of organ-specific proteins in the blood sample from the subject to a previously-determined normal level of each of the plurality of each organ-specific protein; wherein a statistically significant altered level of one or more of the plurality of organ-specific proteins in the blood of the subject as compared to the previously-determined normal level is indicative of the presence or absence of disease. In this regard, the plurality of organ-specific proteins may be detected using any method described herein, such as mass spectrometry or an immunoassay. In one embodiment, the plurality of organ-specific proteins is measured using an antibody array.

A further aspect of the invention provides a method for detecting perturbation of a normal biological state in a subject comprising, a) contacting a blood sample from the subject with a plurality of detection reagents each specific for an organ-specific protein secreted into blood, wherein each organ-specific protein is secreted from the same organ; b) measuring the amount of the organ-specific protein detected in the blood sample by each detection reagent; c) comparing the amount of the organ-specific protein detected in the blood sample by each detection reagent to a predetermined control amount for each respective organ-specific protein; wherein a statistically significant altered level in one or more of the organ-specific proteins indicates a perturbation in the normal biological state.

Another aspect of the invention provides a method for detecting perturbation of a normal biological state in a subject, comprising, a) contacting a blood sample with one or more detection reagents wherein the one or more detection reagents are each specific for an organ-specific protein secreted into blood, wherein the organ-specific proteins are secreted from the same organ; b) measuring the amount of the organ-specific protein detected in the blood sample by the one or more detection reagents; c) comparing the amount of the organ-specific protein detected in the blood sample by the one or more detection reagents to a predetermined control amount for each respective organ-specific protein; wherein a statistically significant altered level in the one or more of the organ-specific proteins indicates a perturbation in the normal biological state. In this regard, the plurality of detection reagents may comprises about 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 detection reagents. In one embodiment, the perturbation from normal comprises perturbation of the prostate the organ-specific proteins are prostate-specific proteins. In another embodiment, the perturbation comprises perturbation of the liver and the organ-specific proteins are liver-specific proteins. In yet a further embodiment, the perturbation comprises perturbation of the breast and the organ-specific proteins are breast-specific proteins. In this regard, the perturbation may comprise a perturbation of any organ as described herein.

Another aspect of the invention provides a diagnostic panel for determining the presence or absence of disease in a subject comprising, a plurality of detection reagents each specific for detecting one of a plurality of organ-specific proteins present in a blood sample; wherein the organ-specific proteins are secreted from the same organ and wherein detection of the plurality of organ-specific proteins with the plurality of detection reagents results in a fingerprint indicative of the presence or absence of disease in the subject. In one embodiment, the detection reagents comprise antibodies or antigen-binding fragments thereof and in certain embodiments, the antibodies or antigen-binding fragments thereof are monoclonal antibodies, or antigen-binding fragments thereof.

A further aspect of the invention provides a diagnostic panel for determining the presence or absence of disease in a subject comprising, one or more detection reagents each specific for detecting an organ-specific protein present in a blood sample; wherein the organ-specific proteins are secreted from the same organ and wherein detection of the one or more organ-specific proteins with the one or more of detection reagents results in a fingerprint indicative of the presence or absence of disease in the subject. In one embodiment, the plurality of detection reagents comprises about 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 detection reagents. In a further embodiment, the organ-specific proteins comprise prostate-specific proteins, liver-specific proteins, breast-specific proteins. In another embodiment, the disease comprises a cancer. In this regard, the cancer may include but is not limited to prostate cancer, ovarian cancer, breast cancer, liver cancer, lung cancer, pancreatic cancer, kidney cancer, or colon cancer. In another embodiment the disease may include, but is not limited to, cardiovascular disease, metabolic disease, infectious disease, genetic disease, autoimmune disease, immune-related disease, neurological disease or cancer.

Another aspect of the invention provides a method for identifying organ-specific proteins secreted or shed into the blood comprising, generating a signature sequence from transcripts from a sample from a specific organ; identifying transcripts that are specifically expressed in the organ; identifying from the transcripts in (b) those transcripts that encode secreted proteins; thereby identifying organ-specific proteins secreted or shed into the blood.

A further aspect of the invention provides a method for identifying organ-specific proteins secreted or shed into the blood comprising, generating a signature sequence from transcripts from a sample from a specific organ; identifying transcripts that are expressed in the specific organ at at least 1.5 fold as compared to the level of expression of the transcript observed in other organs; identifying from the transcripts in (b) those transcripts that encode secreted proteins; thereby identifying organ-specific proteins secreted or shed into the blood.

Another aspect of the invention provides a computer system for processing data relating to organ-specific molecular blood fingerprints, comprising: means operable to receive input identifying an organ-specific molecular blood fingerprint; an organ-specific molecular blood fingerprint database, the organ-specific molecular blood fingerprint database being a computer-readable collection of information about a set of organ-specific molecular blood fingerprints, the set including defined normal blood fingerprints from normal samples and defined disease blood fingerprints from samples from individuals diagnosed with a particular disease; means operable to receive organ-specific fingerprint information from a subject; means operable to use the organ-specific molecular blood fingerprint database and the organ-specific fingerprint information from the subject to match the subject fingerprint to a disease fingerprint, to a normal fingerprint, or to identify a fingerprint that is perturbed from normal but does not match to a disease fingerprint in the database.

BRIEF DESCRIPTION OF THE SEQUENCE IDENTIFIERS

SEQ ID NO:1 is the cDNA sequence that encodes the WDR19 prostate specific secreted protein.

SEQ ID NO:2. is the amino acid sequence of the WDR19 prostate specific secreted protein.

SEQ ID NOs:3-72 are MPSS signature sequences that correspond to differentially expressed genes in LNCaP cells (early prostate cancer phenotype) to androgen-independent CL1 cells (late prostate cancer phenotype) (see Table 1).

SEQ ID NOs:73-593 are MPSS signature sequences that correspond to differentially expressed genes in prostate cancer cell lines LNCaP and CL1 that encode secreted proteins (see Table 3).

SEQ ID NOs:594-1511 are the GENBANK sequences of differentially expressed genes that encode predicted secreted proteins as referred to in Table 3. Both polynucleotide and amino acid sequences are provided for each GENBANK accession number.

SEQ ID NOs:1512-1573 are the amino acid sequences from GENBANK of prostate-specific proteins potentially secreted into blood as described in Table 4.

SEQ ID NOs:1574-1687 are the GENBANK sequences of examples of differentially expressed genes as described in Table 1. Both polynucleotide and amino acid sequences are provided where available for each GENBANK accession number.

SEQ ID NOs:1688-1796 are MPSS signature sequences that correspond to prostate-specific/enriched genes as described in Table 5. SEQ ID NOs:1797-1947 are the GENBANK sequences of prostate-specific genes as described in Table 5. Both polynucleotide and amino acid sequences are provided where available for each GENBANK accession number.

DETAILED DESCRIPTION OF THE INVENTION

A powerful new systems approach to disease is revealing powerful new blood diagnostics approaches. Particularly, in specific cells there are protein and gene regulatory networks that mediate the normal functions of the cell. The disease process causes one or more of these networks to be perturbed, either genetically or environmentally (e.g. infections). The disease-altered networks result in altered patterns of protein expression—and some of the transcripts with altered expression levels are organ (cell)-specific and some of these organ-specific transcripts encode secreted proteins. Thus, disease leads to altered expression patterns of organ-specific, secreted proteins in the blood.

Hence the blood may be viewed as a window into the health and disease of an individual. The levels of organ-specific secreted proteins present in the blood taken together represent molecular fingerprints in the blood that reflect the operation of normal organs. Each organ has a specific quantitative molecular fingerprint. When disease attacks an organ, that blood fingerprint changes, for example, in the levels of these proteins expressed in the blood and the change in the fingerprint correlates with the specific disease. The changes in the fingerprints occur as a consequence of virtually any disease or organ perturbation with each disease fingerprint being unique. The changes in the fingerprints are sufficiently informative to carry out disease stratification, follow the progression of the particular disease stratification or type and follow responses to therapy. These fingerprints also allow one to stratify patients with regard to their ability to respond to particular therapies and even to visualize adverse effects of drugs. The disease fingerprints are determined by comparing the blood from normal individuals against that from patients with specific diseases at known stages. Not only will the absolute levels of the changes in the proteins constituting individual fingerprints be determined, but all the protein changes (e.g. N changed proteins) will be compared against one another to generate an N-dimensional shape space that will correlate even more powerfully with the disease stratifications and progression states described above (see e.g., U.S. Patent Application No. 20020095259).

In the studies described herein, the transcriptomes of two prostate cancer cell lines were analyzed: LNCaP, an androgen sensitive cell line, and hence a model for early stage of prostate cancer; and a variant of this cell, CL1, an androgen unresponsive cell line, thus, a model for late stage of prostate cancer. Analyses of the transcriptomes of these two cell lines revealed changes in cellular states that occur with the progression of prostate cancer. These transcriptomes were also compared to normal prostate tissue, prostate cancer tissues and prostate cancer metastases. These prostate transcriptomes were compared against their counterparts from 29 other tissues to identify those transcripts that are primarily expressed in the prostate. Computational approaches were used to predict which of these transcripts encode secreted proteins. Further, a prostate protein, referred to as WDR19, that was previously shown by microarray and northern analysis to be prostate-specific, was used in a multiparameter analysis of prostate cancer samples.

Thus, the present invention is generally directed to methods for identifying organ-specific secreted proteins present in the blood. The present invention is also directed to methods for defining organ-specific molecular blood fingerprints and further provides defined examples of predicted organ-specific molecular blood fingerprints. Additionally, the present invention is directed to panels of reagents or proteomic techniques employing mass spectrometry that detect organ-specific secreted proteins in the blood for use in diagnostics and other settings.

The blood fingerprints described herein enable physicians to develop a powerful new predictive medicine that can serve as one of the cornerstones for a revolution in medicine, moving it from a reactive mode (treating after the patient is sick) to more predictive, preventive and personalized modes.

By predefining the components of a given molecular blood fingerprint using the methods described herein, the present invention alleviates the need to blindly search for protein patterns using blood proteomics. Thus, the present invention enables the skilled artisan to 1) identify blood proteins which collectively constitute unique molecular blood fingerprints for healthy and diseased individuals; 2) identify unique fingerprints for each different disease; 3) identify fingerprints that can uniquely distinguish the different types of a particular disease (e.g., for prostate cancer, the ability to distinguish between benign disease, slowly growing disease and rapidly metastatic disease); 4) identify fingerprints that can reveal the stage of progression of each type of disease, and 5) fingerprints that will allow one to assess the response to therapy. Importantly, the potential organ-specific, secreted disease-detecting blood fingerprints can be predicted from a combination of quantitative comparative transcriptome studies and computational methods to predict which transcripts encode secreted proteins. The methods for determining the organ-specific, blood fingerprints for all organs described herein allow disease detection at very early stages, since even in the earliest disease stages, the cellular networks which control the expression patterns of these blood molecular signatures will be perturbed. Hence the present invention allows detection of virtually any type of disease and detection of each disease at a very early stage.

Methods for Identifying Organ-Specific Proteins Secreted Into the Blood

The invention provides methods for identifying organ-specific secreted proteins. In this regard, as used herein, the term “organ” is defined as would be understood in the art. Thus, the term, “organ-specific” as used herein generally refers to proteins (or transcripts) that are primarily expressed in a single organ. It should be noted that the skilled artisan would readily appreciate upon reading the instant specification that cell-specific transcripts and proteins and tissue-specific transcripts and proteins are also contemplated in the present invention. As such, and as discussed further herein, in certain embodiments, organ-specific protein is defined as a protein encoded by a transcript that is expressed at a level of at least 3 copies/million (as measured, for example, by massively parallel signature sequencing (MPSS) in the cell/tissue/organ of interest but is expressed at less than 3 copies/million in other cells/tissues/organs. In a further embodiment, an organ-specific protein is one that is encoded by a transcript that is expressed 95% in one organ and the remaining 5% in one or more other organs. (In this context, total expression across all organs examined is taken as 100%).

In certain embodiments, an organ-specific protein is one that is encoded by a transcript that is expressed at about 50%, 55%, 60%, 65%, 70%, 75%, 80% to about 90% in one organ and wherein the remaining 10%-50% is expressed in one or more other organs. As would be readily recognized by the skilled artisan upon reading the present disclosure, in certain embodiments, an organ-specific molecular blood fingerprint can readily be discerned even if some expression of an “organ-specific” protein from a particular organ is detected at some level in another organ, or even more than one organ. For example, the organ-specific molecular blood fingerprint from prostate can conclusively identify a particular prostate disease (and stage of disease) despite expression of one or more protein members of the fingerprint in one or more other organs. Thus, an organ-specific protein as described herein may be predominantly or differentially expressed in an organ of interest rather than uniquely or specifically expressed in the organ. In this regard, in certain embodiments, differentially expressed means at least 1.5 fold expression in the organ of interest as compared to other organs. In another embodiment, differentially expressed means at least 2 fold expression in the organ of interest as compared to expression in other organs. In yet a further embodiment, differentially expressed means at least 2.5, 3, 3.5, 4, 4.5, 5 fold or higher expression in the organ of interest as compared to expression of the protein in other organs. As described elsewhere herein, “protein” expression can be determined by analysis of transcript expression using a variety of methods.

In one embodiment, the organ-specific proteins are identified by preparing a cDNA library from an organ of interest. Any organ of a mammalian body is contemplated herein. Illustrative organs include, but are not limited to, heart, kidney, ureter, bladder, urethra, liver, prostate, heart, blood vessels, bone marrow, skeletal muscle, smooth muscle, brain (amygdala, caudate nucleus, cerebellum, corpus callosum, fetal, hypothalamus, thalamus), spinal cord, peripheral nerves, retina, nose, trachea, lungs, mouth, salivary gland, esophagus, stomach, small intestines, large intestines, hypothalamus, pituitary, thyroid, pancreas, adrenal glands, ovaries, oviducts, uterus, placenta, vagina, mammary glands, testes, seminal vesicles, penis, lymph nodes, PBMC, thymus, and spleen. As noted above, upon reading the present disclosure, the skilled artisan would recognize that cell-specific and tissue-specific proteins are contemplated herein and thus, proteins specifically expressed in cells or tissues that make up such organs are also contemplated herein. In certain embodiments, in each of these organs transcriptomes are obtained for the cell types in which the disease of interest arises. For example, in the prostate there are two dominant types of cells—epithelial cells and stromal cells. About 98% of prostate cancers arise in epithelial cells. As such, in certain embodiments, “organ-specific” means the transcripts that are expressed in particular cell types of the organ of interest (e.g., prostate epithelial cells). In this regard, any cell type that makes up any of the organs described herein is contemplated herein. Illustrative cell types include, but are not limited to, epithelial cells, stromal cells, endothelial cells, endodermal cells, ectodermal cells, mesodermal cells, lymphocytes (e.g., B cells and T cells including CD4+ T helper 1 or T helper 2 type cells, CD8+ cytotoxic T cells), erythrocytes, keratinocytes, and fibroblasts. Particular cell types within organs or tissues may be obtained by histological dissection, by the use of specific cell lines (e.g., prostate epithelial cell lines), by cell sorting or by a variety of other techniques known in the art.

It should be noted that in certain embodiments, fingerprints can be determined from “organ-specific” proteins from multiple organs, such as from organs that share a common function or make up a system (e.g., digestive system, circulatory system, respiratory system, cardiovascular system, the immune system (including the different cells of the immune system, such as, but not limited to, B cells, T cells including CD4+ T helper 1 or T helper 2 type cells, regulatory T cells, CD8+ cytotoxic T cells, NK cells, dendritic cells, macrophages, monocytes, neutrophils, granulocytes, mast cells, etc.), the sensory system, the skin, brain and the nervous system, and the like).

Complementary DNA (cDNA) libraries can be generated using techniques known in the art, such as those described in Ausubel et al. (2001 Current Protocols in Molecular Biology, Greene Publ. Assoc. Inc. & John Wiley & Sons, Inc., NY, N.Y.); Sambrook et al. (1989 Molecular Cloning, Second Ed., Cold Spring Harbor Laboratory, Plainview, N.Y.); Maniatis et al. (1982 Molecular Cloning, Cold Spring Harbor Laboratory, Plainview, N.Y.) and elsewhere. Further, a variety of commercially available kits for constructing cDNA libraries are useful for making the cDNA libraries of the present invention. Libraries are constructed from organs/tissues/cells procured from normal subjects.

All or substantially all of the transcripts of the cDNA library, e.g., representing virtually or substantially all genes functioning in the organ of interest, are cloned and sequenced using any of a variety of techniques known in the art. In this regard, in certain embodiments, substantially all refers to a sample representing at least 80% of all genes functioning in the organ of interest. In a further embodiment, substantially all refers to a sample representing at least 85%, 90%, 95%, 96%, 97%, 98% 99% or higher of all genes functioning in the organ of interest. In one embodiment, substantially all the transcripts from a cDNA library are amplified, sorted and signature sequences generated therefrom according to the methods described in U.S. Pat. Nos. 6,013,445; 6,172,218; 6,172,214; 6,140,489 and Brenner, P., et al., Nat Biotechnol, 18:630-634 2000. Briefly, polynucleotide templates from a cDNA library of interest are cloned into a vector system that contains a vast set of minimally cross-hybridizing oligonucleotide tags (see U.S. Pat. No. 5,863,722). The number of tags is usually at least 100 times greater than the number of cDNA templates (see e.g., U.S. Pat. No. 6,013,445 and Brenner, P., et al., supra). Thus, the set of tags is such that a 1% sample taken of template-tag conjugates ensures that essentially every template in the sample is conjugated to a unique tag and that at least one of each of the different template cDNAs is represented in the sample with >99% probability (U.S. Pat. No. 6,013,445 and Brenner, P., et al., supra). The conjugates are then amplified and hybridized under stringent conditions to microbeads each of which has attached thereto a unique complementary, minimally cross-hybridizing oligonucleotide tag. The transcripts are then directly sequenced simultaneously in a flow cell using a ligation-based sequencing method (see e.g., U.S. Pat. No. 6,013,445). A short signature sequence of about 17-20 base pairs is generated simultaneously from each of the hundreds of thousands of beads (or more) in the flow cell, each having attached thereto copies of a unique transcript from the sample. This technique is termed massively parallel signature sequencing (MPSS).

In certain embodiments, other techniques may be used to evaluate the transcripts from a particular cDNA library, including microarray analysis (Han, M., et al., Nat Biotechnol, 19: 631-635, 2001; Bao, P., et al., Anal Chem, 74: 1792-1797, 2002; Schena et al., Proc. Natl. Acad. Sci. USA 93:10614-19, 1996; and Heller et al., Proc. Natl. Acad. Sci. USA 94:2150-55, 1997) and SAGE (serial analysis of gene expression). Like MPSS, SAGE is digital and can generate a large number of signature sequences. (see e.g., Velculescu, V. E., et al., Trends Genet, 16: 423-425, 2000; Tuteja R. and Tuteja N. Bioessays. 2004 August; 26(8):916-22) although the coverage is not nearly as deep as with MPSS.

The resulting sequences, (e.g., MPSS signature sequences), are generally about 20 bases in length. However, in certain embodiments, the sequences can be about 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 or more bases in length. The sequences are annotated using annotated human genome sequence (such as human genome release hg16, released in November, 2003, or other public or private databases) and the human Unigene (Unigene build #184) using methods known in the art, such as the method described by Meyers, B. C., et al., Genome Res, 14: 1641-1653, 2004. Other databases useful in this regard include Genbank, EMBL, or other publicly available databases. In certain embodiments, transcripts are considered only for those with 100% matches between an MPSS or other type of signature and a genome signature. As would be readily appreciated by the skilled artisan upon reading the present disclosure, this is a stringent match criterion and in certain embodiments, it may be desirable to use less stringent match criteria. Indeed, polymorphisms could lead to variations in transcripts that would be missed if only exact matches were used. For example, it may be desirable to consider signature sequences that match a genome signature with 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% identity. In one embodiment, signatures that are expressed at less than 3 transcripts per million in libraries of interest are disregarded, as they might not be reliably detected since this, in effect, represents less than one transcript per cell (see for example, Jongeneel, C. V., et al., Proc Natl Acad Sci USA, 2003). cDNA signatures are classified by their positions relative to polyadenylation signals and poly (A) tails and by their orientation relative to the 5′→3′ orientation of source mRNA. Full-length sequences corresponding to the signature sequences can be thus identified.

In order to identify organ-specific transcripts, the resulting annotated transcripts are compared against public and/or private sequence databases, such as a variety of annotated human genome sequence databases (e.g., Genebank, the EMBL and Japanese databases and databases generated and compiled from other normal tissues, to identify those transcripts that are expressed primarily in the organ of interest but are not expressed in other organs. As noted elsewhere herein, some expression in organs other than the organ of interest does not necessarily preclude the use of a particular transcript in a blood molecular signature panel of the present invention.

Comparisons of the transcripts between databases can be made using a variety of computer analysis algorithms known in the art. As such, alignment of sequences for comparison may be conducted by the local identity algorithm of Smith and Waterman (1981) Add. APL. Math 2:482, by the identity alignment algorithm of Needleman and Wunsch (1970) J. Mol. Biol. 48:443, by the search for similarity methods of Pearson and Lipman (1988) Proc. Natl. Acad. Sci. USA 85: 2444, by computerized implementations of these algorithms (GAP, BESTFIT, BLAST, FASTA, and TFASTA in the Wisconsin Genetics Software Package, Genetics Computer Group (GCG), 575 Science Dr., Madison, Wis.), or by inspection. As would be understood by the skilled artisan, many algorithms are available and are continually being developed. Appropriate algorithms can be chosen based on the specific needs for the comparisons being made (See also, e.g., J. A. Cuff, et al., Bioinformatics, 16(2):111-116, 2000; S. F Altschul and B. W. Erickson. Bulletin of Mathematical Biology, 48(5/6):603-616, 1986; S. F. Altschul and B. W. Erickson. Bulletin of Mathematical Biology, 48(5/6):633-660, 1986; S. F. Altschul, et al., J. Mol. Bio., 215:403-410, 1990; K. Bucka-Lassen, et al., BIOINFORMATICS, 15(2):122-130, 1999; K.-M. Chao, et al., Bulletin of Mathematical Biology, 55(3):503-524, 1993; W. M. Fitch and T. F. Smith. Proceedings of the National Academy of Sciences, 80:1382-1386, 1983; A. D. Gordon. Biometrika, 60:197-200, 1973; O. Gotoh. J Mol Biol, 162:705-708, 1982; O. Gotoh. Bulletin of Mathematical Biology, 52(3):359-373, 1990; X. Huang, et al., CABIOS, 6:373-381, 1990; X. Huang and W. Miller. Advances in Applied Mathematics, 12:337-357, 1991; J. D. Thompson, et al., Nucleic Acids Research, 27(13):2682-2690, 1999).

In certain embodiments, a particular transcript is considered to be organ-specific when the number of transcripts/million as determined by MPSS is 3 or greater in the organ of interest but is less than 3 in all other organs. In another embodiment, a transcript is considered organ-specific if it is expressed in the organ of interest at a detectable level using a standard measurement (e.g., microarray analysis, quantitative real-time RT-PCR, MPSS, etc.) in the organ of interest but is not detectably expressed in other organs, using appropriate negative and positive controls as would be familiar to the skilled artisan. In a further embodiment, an organ-specific transcript is one that is expressed 95% in one organ and the remaining 5% in one or more other organs. (In this context, total expression across all organs examined is taken as 100%). In certain embodiments, an organ-specific transcript is one that is expressed at about 50%, 55%, 60%, 65%, 70%, 75%, 80% to about 90% in one organ and wherein the remaining 10%-50% is expressed in one or more other organs.

In another embodiment, organ-specific transcripts are identified by determining the ratio of expression of a transcript in the organ of interest as compared to other organs. In this regard, expression levels in the organ of interest of at least 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0 fold or higher as compared to expression in all other organs is considered to be organ-specific expression.

As would be readily recognized by the skilled artisan upon reading the present disclosure, in certain embodiments, an organ-specific molecular blood fingerprint can readily be discerned even if some expression of an “organ-specific” protein from a particular organ is detected at some level in another organ, or even more than one organ. This is because the fingerprint (e.g., the combination of the levels of multiple proteins; the pattern of the expression levels of multiple markers) itself is unique despite that the expression levels of one or more individual members of the fingerprint may not be unique to a particular organ. For example, the organ-specific molecular blood fingerprint from prostate can conclusively identify a particular prostate disease (and stage of disease) despite some expression of one or more members of the fingerprint in one or more other organs. Thus the present invention relates to determining the presence or absence of a disease or condition or stage of disease based on a pattern (e.g., fingerprint) of markers measured concurrently using any one or more of a variety of methods described herein (e.g., antibody binding, mass spectrometry, and the like), rather than the measure of individual markers.

In further embodiments, specificity can be confirmed at the protein level using immunohistochemistry (IHC) and/or other protein measurement techniques known in the art (e.g., isotope-coded affinity tags and mass spectrometry, such as described by Han, D. K., et al., Nat Biotechnol, 19: 946-951, 2001). The Z-test (Man, M. Z., et al., Bioinformatics, 16: 953-959, 2000) or other appropriate statistical tests can be used to calculate P values for comparison of gene and protein expression levels between libraries from organs of interest.

Organ-specific sequences identified as described herein are further analyzed to determine which of the sequences encode secreted proteins. Proteins with signal peptides (classical secretory proteins) can be predicted using computation analysis known in the art. Illustrative methods include, but are not limited to the criteria described by Chen et al., Mamm Genome, 14: 859-865, 2003. In certain embodiments, such analyses are carried out using prediction servers, for example SignalP 3.0 server developed by The Center for Biological Sequence Analysis, Lyngby, Denmark (httpcolon double slash dot www dot cbs dot dtu dot dkslash services slash SignalP-3.0; see also, J. D. Bendtsen, et al., J. Mol. Biol., 340:783-795, 2004.) and the TMHMM2.0 server (see for example A. Krogh, et al., Journal of Molecular Biology, 305(3):567-580, January 2001; E. L. L. Sonnhammer, et al., In J. Glasgow, T. Littlejohn, F. Major, R. Lathrop, D. Sankoff, and C. Sensen, editors, Proceedings of the Sixth International Conference on Intelligent Systems for Molecular Biology, pages 175-182, Menlo Park, Calif., 1998. AAAI Press). Other prediction methods that can be used in the context of the present invention include those described for example, in S. Moller, M. D. R. et al., Bioinformatics, 17(7):646-653, July 2001. Nonclassical secretory secreted proteins (without signal peptides) can be predicted using, for example, the SecretomeP 2.0 server, (http colon double slash www dot cbs dot dtu dot dk slash services slash SecretomeP-2.0 slash/) with an odds ratio score >3.0. Updated versions of these analysis programs are also contemplated for use in the present methods as are other methods known in the art (e.g., PSORT (http colon double slash psort dot nibb dot acdot jp slash/) and Sigfind (http colon double slash 139.91.72.10 slash sigfindslash sigfind dot html).

Confirmation that the identified secreted proteins are present in blood can be carried out using a variety of methods known in the art. For example, the proteins can be expressed, purified, and specific antibodies can be made against them. The specific antibodies can then be used to test the presence of the protein in blood/serum/plasma by a variety of immunoaffinity based techniques (e.g., immunoblot, Western analysis, immunoprecipitation, ELISA, etc.). Antibodies specific for the organ-specific protein identified herein can also be used to study expression patterns of the identified proteins. It should be noted that in certain circumstances, the secreted protein may not be detectable in normal blood samples but will be detected in the blood as a result of perturbation due to disease or other environmental factors. Accordingly, both normal and disease samples are tested for the presence of the secreted protein and particularly for changes in levels of expression in the two states. As an alternative, aptamers (short DNA or RNA fragments with binding complementarity to the proteins of interest) may be used in assays similar to those described for antibodies (see for example, Biotechniques. 2001 February; 30(2):290-2, 294-5; Clinical Chemistry. 1999; 45:1628-1650). In addition, antibodies or aptamers may be used in connection with nanowires to create highly sensitive detections systems (see e.g., J. Heath et al., Science. 2004 Dec. 17; 306(5704):2055-6). In further embodiments, mass spectrometry-based methods can be used to confirm the presence of a particular protein in the blood.

As would be recognized by the skilled artisan, while the organ-specific secreted proteins, the levels of which make up a given fingerprint, need not be isolated, in certain embodiments, it may be desirable to isolate such proteins (e.g., for antibody production). As such, the present invention provides for isolated organ-specific secreted proteins or fragments or portions thereof and polynucleotides that encode such proteins. As used herein, the terms protein and polypeptide are used interchangeably. The terms “polypeptide” and “protein” encompass amino acid chains of any length, including full-length endogenous (i.e., native) proteins and variants of endogenous polypeptides described herein. Illustrative polypeptides of the present invention are described in Table 1 and Tables 3-5, the section entitled “Brief Description of the Sequence Identifiers” and are set forth in the sequence listing. “Variants” are polypeptides that differ in sequence from the polypeptides of the present invention only in substitutions, deletions and/or other modifications, such that either the variants' disease-specific expression patterns are not significantly altered or the polypeptides remain useful for diagnostics/detection of organ-specific blood fingerprints as described herein. For example, modifications to the polypeptides of the present invention may be made in the laboratory to facilitate expression and/or purification and/or to improve immunogenicity for the generation of appropriate antibodies and other binding agents, etc. Modified variants (e.g., chemically modified) of the polypeptides of organ-specific, secreted proteins may be useful herein, (e.g., as standards in mass spectrometry analyses of the corresponding proteins in the blood, and the like). As such, in certain embodiments, the biological function of a variant protein is not relevant for utility in the methods for detection and/or diagnostics described herein. Polypeptide variants generally encompassed by the present invention will typically exhibit at least about 70%, 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% or more identity along its length, to a polypeptide sequence set forth herein. Within a polypeptide variant, amino acid substitutions are usually made at no more than 50% of the amino acid residues in the native polypeptide, and in certain embodiments, at no more than 25% of the amino acid residues. In certain embodiments, such substitutions are conservative. A conservative substitution is one in which an amino acid is substituted for another amino acid that has similar properties, such that one skilled in the art of peptide chemistry would expect the secondary structure and hydropathic nature of the polypeptide to be substantially unchanged. In general, the following amino acids represent conservative changes: (1) ala, pro, gly, glu, asp, gln, asn, ser, thr; (2) cys, ser, tyr, thr; (3) val, ile, leu, met, ala, phe; (4) lys, arg, his; and (5) phe, tyr, trp, his. Thus, a variant may comprise only a portion of a native polypeptide sequence as provided herein. In addition, or alternatively, variants may contain additional amino acid sequences (such as, for example, linkers, tags and/or ligands), usually at the amino and/or carboxy termini. Such sequences may be used, for example, to facilitate purification, detection or cellular uptake of the polypeptide.

When comparing polypeptide sequences, two sequences are said to be “identical” if the sequence of amino acids in the two sequences is the same when aligned for maximum correspondence, as described below. Comparisons between two sequences are typically performed by comparing the sequences over a comparison window to identify and compare local regions of sequence similarity. A “comparison window” as used herein, refers to a segment of at least about 20 contiguous positions, usually 30 to about 75, 40 to about 50, in which a sequence may be compared to a reference sequence of the same number of contiguous positions after the two sequences are optimally aligned.

Optimal alignment of sequences for comparison may be conducted using the Megalign program in the Lasergene suite of bioinformatics software (DNASTAR, Inc., Madison, Wis.), using default parameters. This program embodies several alignment schemes described in the following references: Dayhoff, M. O. (1978) A model of evolutionary change in proteins—Matrices for detecting distant relationships. In Dayhoff, M. O. (ed.) Atlas of Protein Sequence and Structure, National Biomedical Research Foundation, Washington D.C. Vol. 5, Suppl. 3, pp. 345-358; Hein J. (1990) Unified Approach to Alignment and Phylogenes pp. 626-645 Methods in Enzymology vol. 183, Academic Press, Inc., San Diego, Calif.; Higgins, D. G. and Sharp, P. M. (1989) CABIOS 5:151-153; Myers, E. W. and Muller W. (1988) CABIOS 4:11-17; Robinson, E. D. (1971) Comb. Theor 11:105; Saitou, N. Nei, M. (1987) Mol. Biol. Evol. 4:406-425; Sneath, P. H. A. and Sokal, R. R. (1973) Numerical Taxonomy—the Principles and Practice of Numerical Taxonomy, Freeman Press, San Francisco, Calif.; Wilbur, W. J. and Lipman, D. J. (1983) Proc. Natl. Acad., Sci. USA 80:726-730.

Alternatively, optimal alignment of sequences for comparison may be conducted by the local identity algorithm of Smith and Waterman (1981) Add. APL. Math 2:482, by the identity alignment algorithm of Needleman and Wunsch (1970) J. Mol. Biol. 48:443, by the search for similarity methods of Pearson and Lipman (1988) Proc. Natl. Acad. Sci. USA 85: 2444, by computerized implementations of these algorithms (GAP, BESTFIT, BLAST, FASTA, and TFASTA in the Wisconsin Genetics Software Package, Genetics Computer Group (GCG), 575 Science Dr., Madison, Wis.), or by inspection.

Illustrative examples of algorithms that are suitable for determining percent sequence identity and sequence similarity include the BLAST and BLAST 2.0 algorithms, which are described in Altschul et al. (1977) Nucl. Acids Res. 25:3389-3402 and Altschul et al. (1990) J. Mol. Biol. 215:403-410, respectively. BLAST and BLAST 2.0 can be used, for example, to determine percent sequence identity for the polynucleotides and polypeptides of the invention. Software for performing BLAST analyses is publicly available through the National Center for Biotechnology Information.

An isolated polypeptide is one that is removed from its original environment. For example, a naturally occurring protein or polypeptide is isolated if it is separated from some or all of the coexisting materials in the natural system. In certain embodiments, such polypeptides are also purified, e.g., are at least about 90% pure, in some embodiments, at least about 95% pure and in further embodiments, at least about 99% pure.

In one embodiment of the present invention, a polypeptide comprises a fusion protein comprising an organ-specific secreted polypeptide. The present invention further provides, in other aspects, fusion proteins that comprise at least one polypeptide as described herein, as well as polynucleotides encoding such fusion proteins. The fusion proteins may comprise multiple polypeptides or portions/variants thereof, as described herein, and may further comprise one or more polypeptide segments for facilitating the expression, purification, detection, and/or activity of the polypeptide(s).

In certain embodiments, the proteins and/or polynucleotides, and/or fusion proteins are provided in the form of compositions, e.g., pharmaceutical compositions, vaccine compositions, compositions comprising a physiologically acceptable carrier or excipient. Such compositions may comprise buffers such as neutral buffered saline, phosphate buffered saline and the like; carbohydrates such as glucose, mannose, sucrose or dextrans, mannitol; proteins; polypeptides or amino acids such as glycine; antioxidants; chelating agents such as EDTA or glutathione; adjuvants (e.g., aluminum hydroxide); and preservatives.

In general, organ-specific secreted polypeptides and polynucleotides encoding such polypeptides as described herein, may be prepared using any of a variety of techniques that are well known in the art. For example, a DNA sequence encoding an organ-specific secreted protein may be prepared by amplification from a suitable cDNA or genomic library using, for example, polymerase chain reaction (PCR) or hybridization techniques. Libraries may generally be prepared and screened using methods well known to those of ordinary skill in the art, such as those described in Sambrook et al., Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratories, Cold Spring Harbor, N.Y., 1989. cDNA libraries may be prepared from any of a variety of organs, tissues, cells, as described herein. Other libraries that may be employed will be apparent to those of ordinary skill in the art upon reading the present disclosure. Primers for use in amplification may be readily designed based on the polynucleotide sequences encoding organ-specific polypeptides as provided herein, for example, using programs such as the PRIMER3 program (http colon double slash www-genome dot wi dot mit dot edu/cgi-bin/primer/primer3_www dot cgi).

Polynucleotides encoding the organ-specific secreted polypeptides as described herein are also provided by the present invention. A polynucleotide as used herein may be single-stranded (coding or antisense) or double-stranded, and may be DNA (genomic, cDNA or synthetic) or RNA molecules. Thus, within the context of the present invention, a polynucleotide encoding a polypeptide may also be a gene. A gene is a segment of DNA involved in producing a polypeptide chain; it includes regions preceding and following the coding region (leader and trailer) as well as intervening sequences (introns) between individual coding segments (exons). Additional coding or non-coding sequences may, but need not, be present within a polynucleotide of the present invention, and a polynucleotide may, but need not, be linked to other molecules and/or support materials. An isolated polynucleotide, as used herein, means that a polynucleotide is substantially away from other coding sequences, and that the DNA molecule does not contain large portions of unrelated coding DNA, such as large chromosomal fragments or other functional genes or polypeptide coding regions. Of course, this refers to the DNA molecule as originally isolated, and does not exclude genes or coding regions later added to the segment by the hand of man.

Polynucleotides of the present invention may comprise a native sequence (i.e., an endogenous polynucleotide, for instance, a native or non-artificially engineered or naturally occurring gene as provided herein) encoding an organ-specific secreted protein, an alternate form of such a sequence, or a portion or splice variant thereof or may comprise a variant of such a sequence. Polynucleotide variants may contain one or more substitutions, additions, deletions and/or insertions such that the polynucleotide encodes a polypeptide useful in the methods described herein, such as for the detection of organ-specific proteins (e.g., wherein said polynucleotide variants encode polypeptides that can be used to generate detection reagents as described herein that are specific for an organ-specific secreted protein). In certain embodiments, variants exhibit at least about 70% identity, and in other embodiments, exhibit at least about 80%, 85%, 86%, 87%, 88%, 89%, identity and in yet further embodiments, at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identity to a polynucleotide sequence that encodes a native organ-specific secreted polypeptide or an alternate form or a portion thereof. Illustrative polynucleotides of the present invention are described in Table 1 and Tables 3-5, the section entitled “Brief Description of the Sequence Identifiers” and are set forth in the sequence listing. The percent identity may be readily determined by comparing sequences using computer algorithms well known to those having ordinary skill in the art and described herein.

Polynucleotides that are complementary to the polynucleotides described herein, or that have substantial identity to a sequence complementary to a polynucleotide as described herein are also within the scope of the present invention. “Substantial identity”, as used herein refers to polynucleotides that exhibit at least about 70% identity, and in certain embodiments, at least about 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identity to a polynucleotide sequence that encodes a native organ-specific secreted polypeptide as described herein. Substantial identity can also refer to polynucleotides that are capable of hybridizing under stringent conditions to a polynucleotide complementary to a polynucleotide encoding an organ-specific secreted protein. Suitable hybridization conditions include prewashing in a solution of 5×SSC, 0.5% SDS, 1.0 mM EDTA (pH 8.0); hybridizing at 50-65° C., 5×SSC, overnight; followed by washing twice at 65° C. for 20 minutes with each of 2×, 0.5× and 0.2×SSC containing 0.1% SDS. Nucleotide sequences that, because of code degeneracy, encode a polypeptide encoded by any of the above sequences are also encompassed by the present invention.

Oligonucleotide primers for amplification of the polynucleotides encoding organ-specific secreted proteins are also within the scope of the present invention. Many amplification methods are known in the art such as PCR, RT-PCR, quantitative real-time PCR, and the like. The PCR conditions used can be optimized in terms of temperature, annealing times, extension times and number of cycles depending on the oligonucleotide and the polynucleotide to be amplified. Such techniques are well known in the art and are described in, for example, Mullis et al., Cold Spring Harbor Symp. Quant. Biol., 51:263, 1987; Erlich ed., PCR Technology, Stockton Press, NY, 1989. Oligonucleotide primers can be anywhere from 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 nucleotides in length. In certain embodiments, the oligonucleotide primers of the present invention are typically 35, 40, 45, 50, 55, 60, or more nucleotides in length.

Organ-Specific Molecular Blood Fingerprints

The present invention also provides methods for defining organ-specific molecular blood fingerprints. Additionally, the present invention provides defined examples of organ-specific molecular blood fingerprints as described further herein.

Each normal organ controls the expression of a variety of genes, some of which are expressed at major levels at other organs or tissues in the body and some of which are expressed only in the organ of interest or at significantly increased levels in the organ of interest as compared to expression in other organs/tissues (e.g., at least 2 fold, at least 2.5 fold, at least 3.0 fold, at least 3.5 fold, at least 4.0 fold, at least 4.5 fold, or higher fold expression in the organ of interest as compared to other tissues. Some of the organ-specific transcripts encode proteins which can be secreted into the blood. Hence these secreted proteins constitute an organ-specific molecular fingerprint for that organ in the blood. Analysis of levels of these proteins in the blood provides organ-specific molecular blood fingerprints that are indicative of biological states. A biological state may be a normal, healthy state or a disease state (e.g., perturbation from normal). Thus, there are molecular fingerprints in the blood that reflect the operation of normal organs and each organ has a specific molecular fingerprint. These organ-specific blood fingerprints are perturbed when disease, or other agents such as drugs, affects the organ. Different diseases will alter the organ-specific blood fingerprints in different ways (e.g. alter the expression levels of the corresponding secreted proteins). Thus, a unique perturbed blood molecular fingerprint is associated with each type of distinct disease. In effect, each distinct disease, or stage of a disease, creates its own molecular blood fingerprint for each organ that it affects. As would be readily appreciated by the skilled artisan, each disease or stage of a disease can affect multiple organs. For example, in kidney cancer, a primary perturbation in the kidney-specific molecular blood fingerprint would occur. However, a secondary or indirect effect may also be observed in the bladder-specific molecular blood fingerprint. As another example, in liver cancer, perturbation of a liver-specific blood fingerprint as a primary indicator of disease would occur. However, secondary or indirect effects at other sites, for example in a lymphocyte-specific blood fingerprint, would also be observed. As described elsewhere herein, each disease type and stage results in a unique, identifiable fingerprint for each organ that it affects, for primary and secondary organs affected. Thus, multiple organ-specific molecular blood fingerprints can be used in combination to determine a particular biological state and the fingerprints may include those for the primary organ affected and/or for a secondary or indirect organ that is affected by a particular disease.

Most common diseases such as prostate cancer actually represent multiple distinct diseases that initially appear similar (e.g., benign and very slowly growing prostate cancer, slowly invasive prostate cancer and rapidly metastatic prostate cancer represent three different types of prostate cancer—the process of dividing individual prostate cancers into one of these three types is called stratification). The blood molecular fingerprints will be distinct for each of these disease types, thus allowing for the stratification of similar diseases and rapid intervention where necessary. The blood fingerprints will also be perturbed in unique ways as each type of disease progresses—hence the blood fingerprints will also permit the progression of disease to be followed. The blood fingerprints also change with therapy, and hence will permit the effectiveness of therapy to be followed, thereby allowing a physician to alter treatment accordingly. Further, the blood fingerprints change with exposure to a variety of environmental factors, such as drugs, and can be used to assess toxic or off target damage by the drug and it will even permit following the subsequent recovery from such adverse drug exposure.

Thus, an organ-specific molecular blood fingerprint for a given setting (e.g., a particular disease) is defined by the levels in the blood of the organ-specific proteins that make up the fingerprint. As such, an organ-specific molecular blood fingerprint for a given organ at any given time and in any given disease setting is determined by measuring the levels of each of a plurality of organ-specific proteins in the blood. It is the combination of the different levels in the blood of the organ-specific proteins that reveals a unique pattern that defines the fingerprint. Equally important, each of the levels of the proteins can be compared against one another to create an N-dimensional measure of the fingerprint space, a very powerful correlate to health and disease (see e.g., U.S. Patent Application No 20020095259). It should be noted that, in certain embodiments, an organ-specific molecular blood fingerprint may be comprised of the determined level in the blood of one or more organ-specific secreted proteins. In one embodiment, an organ-specific molecular blood fingerprint may comprise the determined level in the blood of anywhere from at least 1 to more than about 100, 200 or more organ-specific secreted proteins from a particular organ of interest. In one embodiment, the organ-specific molecular blood fingerprint comprises the quantitatively measured level in the blood of at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 organ-specific secreted proteins. In another embodiment, the organ-specific molecular blood fingerprint comprises the determined level in the blood of at least, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, or 30 organ-specific secreted proteins. In a further embodiment, the organ-specific molecular blood fingerprint comprises the determined level in the blood of at least, 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40 organ-specific secreted proteins. In yet a further embodiment, the organ-specific molecular blood fingerprint comprises the determined level in the blood of at least, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50 organ-specific secreted proteins. In an additional embodiment, the organ-specific molecular blood fingerprint comprises the determined level in the blood of 51, 52, 53, 54, 55, 56, 57, 58, 59, or 60 organ-specific secreted proteins. In another embodiment, the organ-specific molecular blood fingerprint comprises the determined level in the blood of 61, 62, 63, 64, 65, 66, 67, 68, 69, or 70 organ-specific secreted proteins. In further embodiments, the organ-specific molecular blood fingerprint comprises the determined level in the blood of 75, 80, 85, 90, 100, or more organ-specific secreted proteins.

It should be noted that in certain circumstances, an organ-specific molecular blood fingerprint can be defined (in part or entirely) merely by the presence or absence of one or a plurality of organ-specific proteins, and determining the exact level of each of a plurality of organ-specific proteins in the blood may not be necessary.

In a further embodiment, the disease (e.g., perturbed) molecular blood fingerprints for a particular organ are determined by comparing the blood from normal individuals against that from patients with specific diseases at known stages. A statistically significant change in the levels (e.g., an increase or a decrease) of one or more of the organ-specific proteins in the blood that comprise the fingerprint as compared to normal is indicative of a perturbation of the fingerprint and is useful in diagnostics of the particular disease and/or stage of disease. As discussed elsewhere herein, the fingerprint may be for the primary organ affected by the particular disease of interest, or a secondarily, indirectly affected organ. The skilled artisan would readily appreciate that a variety of statistical tests can be used to determine if an altered level of a given protein is significant. The Z-test (Man, M. Z., et al., Bioinformatics, 16: 953-959, 2000) or other appropriate statistical tests can be used to calculate P values for comparison of protein expression levels. In certain embodiments, the level of each of the plurality of organ-specific proteins in the blood sample from the subject is compared to a previously determined normal control level of each of the plurality of organ-specific proteins taking into account standard deviation. Thus, the present invention provides determined normal control levels of each of a plurality of organ-specific proteins that make up a particular molecular blood fingerprint.

Organ-specific molecular blood fingerprints can be determined using any of a variety of detection reagents in the context of a variety of methods for measuring protein levels. Any detection reagent that can specifically bind to or otherwise detect an organ-specific secreted protein as described herein is contemplated as a suitable detection reagent. Illustrative detection reagents include, but are not limited to antibodies, or antigen-binding fragments thereof, yeast ScFv, DNA or RNA aptamers, isotope labeled peptides, microfluidic/nanotechnology measurement devices and the like.

In one illustrative embodiment, a detection reagent is an antibody or an antigen-binding fragment thereof. Antibodies may be prepared by any of a variety of techniques known to those of ordinary skill in the art. See, e.g., Harlow and Lane, Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory, 1988. In general, antibodies can be produced by cell culture techniques, including the generation of monoclonal antibodies as described herein, or via transfection of antibody genes into suitable bacterial or mammalian cell hosts, in order to allow for the production of recombinant antibodies. In one technique, an immunogen comprising the polypeptide is initially injected into any of a wide variety of mammals (e.g., mice, rats, rabbits, sheep or goats). In this step, the polypeptides of this invention may serve as the immunogen without modification. Alternatively, particularly for relatively short polypeptides, a superior immune response may be elicited if the polypeptide is joined to a carrier protein, such as bovine serum albumin or keyhole limpet hemocyanin. The immunogen is injected into the animal host, usually according to a predetermined schedule incorporating one or more booster immunizations, and the animals are bled periodically. Polyclonal antibodies specific for the polypeptide may then be purified from such antisera by, for example, affinity chromatography using the polypeptide coupled to a suitable solid support.

In one embodiment, multiple target proteins or peptides are used in a single immune response to generate multiple useful detection reagents simultaneously. In one embodiment, the individual specificities are later separated out.

In certain embodiments, antibody can be generated by phage display methods (such as described by Vaughan, T. J., et al., Nat Biotechnol, 14: 309-314, 1996; and Knappik, A., et al., Mol Biol, 296: 57-86, 2000); ribosomal display (such as described in Hanes, J., et al., Nat Biotechnol, 18: 1287-1292, 2000), or periplasmic expression in E. coli (see e.g., Chen, G., et al., Nat Biotechnol, 19: 537-542, 2001.). In further embodiments, antibodies can be isolated using a yeast surface display library. See e.g., nonimmune library of 10⁹ human antibody scFv fragments as constructed by Feldhaus, M. J., et al., Nat Biotechnol, 21: 163-170, 2003. There are several advantages of this yeast surface display compared to more traditional large nonimmune human antibody repertoires such as phage display, ribosomal display, and periplasmic expression in E. coli 1). The yeast library can be amplified 10¹⁰-fold without measurable loss of clonal diversity and repertoire bias as the expression is under control of the tightly GAL1/10 promoter and expansion can be done under non induction conditions; 2) nanomolar-affinity scFvs can be routinely obtained by magnetic bead screening and flow-cytometric sorting, thus greatly simplified the protocol and capacity of antibody screening; 3) with equilibrium screening, a minimal affinity threshold of the antibodies desired can be set; 4) the binding properties of the antibodies can be quantified directly on the yeast surface; 5) multiplex library screening against multiple antigens simultaneously is possible; and 6) for applications demanding picomolar affinity (e.g. in early diagnosis), subsequent rapid affinity maturation (Kieke, M. C., et al., J Mol Biol, 307: 1305-1315, 2001.) can be carried out directly on yeast clones without further re-cloning and manipulations.

Monoclonal antibodies specific for an organ-specific secreted polypeptide of interest may be prepared, for example, using the technique of Kohler and Milstein, Eur. J. Immunol. 6:511-519, 1976, and improvements thereto. Briefly, these methods involve the preparation of immortal cell lines capable of producing antibodies having the desired specificity (i.e., reactivity with the polypeptide of interest). Such cell lines may be produced, for example, from spleen cells obtained from an animal immunized as described above. The spleen cells are then immortalized by, for example, fusion with a myeloma cell fusion partner, in certain embodiments, one that is syngeneic with the immunized animal. A variety of fusion techniques may be employed. For example, the spleen cells and myeloma cells may be combined with a nonionic detergent for a few minutes and then plated at low density on a selective medium that supports the growth of hybrid cells, but not myeloma cells. An illustrative selection technique uses HAT (hypoxanthine, aminopterin, thymidine) selection. After a sufficient time, usually about 1 to 2 weeks, colonies of hybrids are observed. Single colonies are selected and their culture supernatants tested for binding activity against the polypeptide. Hybridomas having high reactivity and specificity are preferred.

Monoclonal antibodies may be isolated from the supernatants of growing hybridoma colonies. In addition, various techniques may be employed to enhance the yield, such as injection of the hybridoma cell line into the peritoneal cavity of a suitable vertebrate host, such as a mouse. Monoclonal antibodies may then be harvested from the ascites fluid or the blood. Contaminants may be removed from the antibodies by conventional techniques, such as chromatography, gel filtration, precipitation, and extraction. The polypeptides of this invention may be used in the purification process in, for example, an affinity chromatography step.

A number of therapeutically useful molecules are known in the art which comprise antigen-binding sites that are capable of exhibiting immunological binding properties of an antibody molecule. The proteolytic enzyme papain preferentially cleaves IgG molecules to yield several fragments, two of which (the “F(ab)” fragments) each comprise a covalent heterodimer that includes an intact antigen-binding site. The enzyme pepsin is able to cleave IgG molecules to provide several fragments, including the “F(ab′)₂” fragment which comprises both antigen-binding sites. An “Fv” fragment can be produced by preferential proteolytic cleavage of an IgM, and on rare occasions IgG or IgA immunoglobulin molecule. Fv fragments are, however, more commonly derived using recombinant techniques known in the art. The Fv fragment includes a non-covalent V_(H)::V_(L) heterodimer including an antigen-binding site which retains much of the antigen recognition and binding capabilities of the native antibody molecule. Inbar et al. (1972) Proc. Nat. Acad. Sci. USA 69:2659-2662; Hochman et al. (1976) Biochem 15:2706-2710; and Ehrlich et al. (1980)Biochem 19:4091-4096.

A single chain Fv (“sFv”) polypeptide is a covalently linked V_(H)::V_(L) heterodimer which is expressed from a gene fusion including V_(H)- and V_(L)-encoding genes linked by a peptide-encoding linker. Huston et al. (1988) Proc. Nat. Acad. Sci. USA 85(16):5879-5883. A number of methods have been described to discern chemical structures for converting the naturally aggregated—but chemically separated—light and heavy polypeptide chains from an antibody V region into an sFv molecule which will fold into a three dimensional structure substantially similar to the structure of an antigen-binging site. See, e.g., U.S. Pat. Nos. 5,091,513 and 5,132,405, to Huston et al.; and U.S. Pat. No. 4,946,778, to Ladner et al.

Each of the above-described molecules includes a heavy chain and a light chain CDR set, respectively interposed between a heavy chain and a light chain FR set which provide support to the CDRS and define the spatial relationship of the CDRs relative to each other. As used herein, the term “CDR set” refers to the three hypervariable regions of a heavy or light chain V region. Proceeding from the N-terminus of a heavy or light chain, these regions are denoted as “CDR1,” “CDR2,” and “CDR3” respectively. An antigen-binding site, therefore, includes six CDRs, comprising the CDR set from each of a heavy and a light chain V region. A polypeptide comprising a single CDR, (e.g., a CDR1, CDR2 or CDR3) is referred to herein as a “molecular recognition unit.” Crystallographic analysis of a number of antigen-antibody complexes has demonstrated that the amino acid residues of CDRs form extensive contact with bound antigen, wherein the most extensive antigen contact is with the heavy chain CDR3. Thus, the molecular recognition units are primarily responsible for the specificity of an antigen-binding site.

As used herein, the term “FR set” refers to the four flanking amino acid sequences which frame the CDRs of a CDR set of a heavy or light chain V region. Some FR residues may contact bound antigen; however, FRs are primarily responsible for folding the V region into the antigen-binding site, particularly the FR residues directly adjacent to the CDRS. Within FRs, certain amino residues and certain structural features are very highly conserved. In this regard, all V region sequences contain an internal disulfide loop of around 90 amino acid residues. When the V regions fold into a binding-site, the CDRs are displayed as projecting loop motifs which form an antigen-binding surface. It is generally recognized that there are conserved structural regions of FRs which influence the folded shape of the CDR loops into certain “canonical” structures—regardless of the precise CDR amino acid sequence. Further, certain FR residues are known to participate in non-covalent interdomain contacts which stabilize the interaction of the antibody heavy and light chains.

The detection reagents of the present invention may comprise any of a variety of detectable labels. The invention contemplates the use of any type of detectable label, including, e.g., visually detectable labels, fluorophores, and radioactive labels. The detectable label may be incorporated within or attached, either covalently or non-covalently, to the detection reagent.

Methods for measuring organ-specific protein levels from blood/serum/plasma include, but are not limited to, immunoaffinity based assays such as ELISAs, Western blots, and radioimmunoassays, and mass spectrometry based methods (matrix-assisted laser desorption ionization (MALDI), MALDI-Time-of-Flight (TOF), Tandem MS (MS/MS), electrospray ionization (ESI), Surface Enhanced Laser Desorption Ionization (SELDI)-TOF MS, liquid chromatography (LC)-MS/MS, etc). Other methods useful in this context include isotope-coded affinity tag (ICAT) followed by multidimensional chromatography and MS/MS. The procedures described herein for analysis of blood organ-specific protein fingerprints can be modified and adapted to make use of microfluidics and nanotechnology in order to miniaturize, parallelize, integrate and automate diagnostic procedures (see e.g., L. Hood, et al., Science 306:640-643; R. H. Carlson, et al., Phys. Rev. Lett. 79:2149 (1997); A. Y. Fu, et al., Anal. Chem. 74:2451 (2002); J. W. Hong, et al., Nature Biotechnol. 22:435 (2004); A. G. Hadd, et al., Anal. Chem. 69:3407 (1997); I. Karube, et al., Ann. N.Y. Acad. Sci. 750:101 (1995); L. C. Waters et al., Anal. Chem. 70:158 (1998); J. Fritz et al., Science 288, 316 (2000)).

It should be noted that when the term “blood” is used herein, any part of the blood is intended. Accordingly, for determining molecular blood fingerprints, whole blood may be used directly where appropriate, or plasma or serum may be used.

Panels/Arrays for Detecting Organ-Specific Molecular Blood Fingerprints

The present invention also provides panels/arrays for detecting the organ-specific blood fingerprints at any given time in a subject. The term “subject” is intended to include any mammal or indeed any vertebrate that may be used as a model system for human disease. Examples of subjects include humans, monkeys, apes, dogs, cats, mice, rats, fish, zebra fish, birds, horses, pigs, cows, sheep, goats, chickens, ducks, donkeys, turkeys, peacocks, chinchillas, ferrets, gerbils, rabbits, guinea pigs, hamsters and transgenic species thereof. Further subjects contemplated herein include, but are not limited to, reptiles and amphibians, e.g., lizards, snakes, turtles, frogs, toads, salamanders, and newts. In one embodiment, the panel/array of the present invention comprises one detection reagent that specifically detects an organ-specific secreted protein. In another embodiment, the panel/arrays are comprised of a plurality of detection reagents that each specifically detects an organ-specific secreted protein, wherein the levels of organ-specific secreted proteins taken together form a unique pattern that defines the fingerprint. In certain embodiments, detection reagents can be bispecific such that the panel/array is comprised of a plurality of bispecific detection reagents that may specifically detect more than one organ-specific secreted protein. The term “specifically” is a term of art that would be readily understood by the skilled artisan to mean, in this context, that the protein of interest is detected by the particular detection reagent but other proteins are not detected in a statistically significant manner under the same conditions. Specificity can be determined using appropriate positive and negative controls and by routinely optimizing conditions.

The panel/arrays may be comprised of a solid phase surface having attached thereto a plurality of detection reagents each attached at a distinct location. As would be recognized by the skilled artisan, the number of detection reagents on a given panel/array would be determined from the number of organ-specific secreted proteins in the fingerprint to be measured. In one embodiment, the panel/array comprises one or more detection reagents. In a further embodiment, the panel/array comprises a plurality of detection reagents, wherein the plurality of detection reagents may be anywhere from about 2 to about 100, 150, 160, 170, 180, 190, 200 or more detection reagents each specific for an organ-specific secreted protein. In one embodiment, the panel/array comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 detection reagents each specific for one of the plurality of organ-specific secreted proteins that make up a given fingerprint. In another embodiment, the panel/array comprises at least 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 detection reagents each specific for one of the plurality of organ-specific secreted proteins that make up a given fingerprint. In a further embodiment, the panel/array comprises at least 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 detection reagents each specific for one of the plurality of organ-specific secreted proteins that make up a given fingerprint. In an additional embodiment, the panel/array comprises at least 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40 detection reagents each specific for one of the plurality of organ-specific secreted proteins that make up a given fingerprint. In yet a further embodiment, the panel/array comprises at least 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50 detection reagents each specific for one of the plurality of organ-specific secreted proteins that make up a given fingerprint. In an additional embodiment, the panel/array comprises at least 51, 52, 53, 54, 55, 56, 57, 58, 59, or 60 detection reagents each specific for one of the plurality of organ-specific secreted proteins that make up a given fingerprint. In one embodiment, the panel/array comprises at least 61, 62, 63, 64, 65, 66, 67, 68, 69, or 70 detection reagents each specific for one of the plurality of organ-specific secreted proteins that make up a given fingerprint. In one embodiment, the panel/array comprises at least 75, 80, 85, 90, 100, 150, 160, 170, 180, 190, 200, or more, detection reagents each specific for one of the plurality of organ-specific secreted proteins that make up a given fingerprint.

Further in this regard, the solid phase surface may be of any material, including, but not limited to, plastic, polycarbonate, polystyrene, polypropylene, polyethlene, glass, nitrocellulose, dextran, nylon, metal, silicon and carbon nanowires, nanoparticles that can be made of a variety of materials and photolithographic materials. In certain embodiments, the solid phase surface is a chip. In another embodiment, the solid phase surface may comprise microtiter plates, beads, membranes, microparticles, the interior surface of a reaction vessel such as a test tube or other reaction vessel. In other embodiments the peptides will be fractionated by one or more one-dimensional columns using size separations, ion exchange or hydrophobicity properties and, for example, deposited in a MALDI 96 or 384 well plate and then injected into an appropriate mass spectrometer.

In one embodiment, the panel/array is an addressable array. As such, the addressable array may comprise a plurality of distinct detection reagents, such as antibodies or aptamers, attached to precise locations on a solid phase surface, such as a plastic chip. The position of each distinct detection reagent on the surface is known and therefore “addressable”. In one embodiment, the detection reagents are distinct antibodies that each have specific affinity for one of a plurality of organ-specific polypeptides.

In one embodiment, the detection reagents, such as antibodies, are covalently linked to the solid surface, such as a plastic chip, for example, through the Fc domains of antibodies. In another embodiment, antibodies are adsorbed onto the solid surface. In a further embodiment, the detection reagent, such as an antibody, is chemically conjugated to the solid surface. In a further embodiment, the detection reagents are attached to the solid surface via a linker. In certain embodiments, detection with multiple specific detection reagents is carried out in solution.

Methods of constructing protein arrays, including antibody arrays, are known in the art (see, e.g., U.S. Pat. No. 5,489,678; U.S. Pat. No. 5,252,743; Blawas and Reichert, 1998, Biomaterials 19:595-609; Firestone et al., 1996, J. Amer. Chem. Soc. 18, 9033-9041; Mooney et al., 1996, Proc. Natl. Acad. Sci. 93,12287-12291; Pirrung et al, 1996, Bioconjugate Chem. 7, 317-321; Gao et al, 1995, Biosensors Bioelectron 10, 317-328; Schena et al, 1995, Science 270, 467-470; Lom et al., 1993, J. Neurosci. Methods, 385-397; Pope et al., 1993, Bioconjugate Chem. 4, 116-171; Schramm et al., 1992, Anal. Biochem. 205, 47-56; Gombotz et al., 1991, J. Biomed. Mater. Res. 25, 1547-1562; Alarie et al., 1990, Analy. Chim. Acta 229, 169-176; Owaku et al, 1993, Sensors Actuators B, 13-14, 723-724; Bhatia et al., 1989, Analy. Biochem. 178, 408-413; Lin et al., 1988, IEEE Trans. Biomed. Engng., 35(6), 466-471).

In one embodiment, the detection reagents, such as antibodies, are arrayed on a chip comprised of electronically activated copolymers of a conductive polymer and the detection reagent. Such arrays are known in the art (see e.g., U.S. Pat. No. 5,837,859 issued Nov. 17, 1998; PCT publication WO 94/22889 dated Oct. 13, 1994). The arrayed pattern may be computer generated and stored. The chips may be prepared in advance and stored appropriately. The antibody array chips can be regenerated and used repeatedly.

Using the methods described herein, a vast array of organ-specific molecular blood fingerprints can be defined for any of a variety of diseases as described further herein. As such, the present invention further provides information databases comprising data that make up molecular blood fingerprints as described herein. As such, the databases may comprise the defined differential expression levels as determined using any of a variety of methods such as those described herein, of each of the plurality of organ-specific secreted proteins that make up a given fingerprint in any of a variety of settings (e.g., normal or disease fingerprints).

Methods of Use

The present invention provides methods for identifying organ-specific secreted proteins and methods for identifying organ-specific molecular blood fingerprints. The present invention further provides panels+/arrays of detection reagents for detecting such fingerprints. The present invention also provides defined organ-specific molecular blood fingerprints for normal and disease settings. As such, the present invention provides methods of detecting diseases. The invention further provides methods for stratifying disease types and for monitoring the progression of a disease. The present invention also provides for following responses to therapy in a variety of disease settings and methods for detecting the disease state in humans using the visualization of nanoparticles with appropriate reporter groups and organ-specific antibodies or aptamers.

The present invention can be used as a standard screening test. In this regard, one or more of the detection panel/arrays described herein can be run on an individual and any statistically significant deviation from a normal organ-specific molecular blood fingerprint would indicate that disease-related perturbation was present. Thus, the present invention provides a standard or “normal” blood fingerprint for any given organ. In certain embodiments, a normal blood fingerprint is determined by measuring the normal range of levels of the individual protein members of a fingerprint. Any deviation therefrom or perturbation of the normal fingerprint that is outside the standard deviation (normal range) has diagnostic utility (see also U.S. Patent Application No. 0020095259). As would be recognized by the skilled artisan, the significance of any deviation in the levels of (e.g., a significantly altered level of one or more of) the individual protein members of a fingerprint can be determined using statistical methods known in the art and described herein. As noted elsewhere herein, perturbation of the normal fingerprint can indicate primary disease of the organ being tested or secondary, indirect affects on that organ resulting from disease of another organ.

In an additional embodiment, the present invention can be used to determine distinct normal organ-specific molecular fingerprints, such as in different populations of people. In this regard, distinct normal patterns of organ-specific molecular blood fingerprints may have differences in populations of patients that permit one to stratify patients into classes that would respond to a particular therapeutic regimen and those which would not.

In a further embodiment, the present invention can be used to determine the risk of developing a particular biological condition. A statistically significant alteration (e.g., increase or decrease) in the levels of one or more members of a particular molecular blood fingerprint may signify a risk of developing a particular disease, such as a cancer, an autoimmune disease, or other biological condition.

To monitor the progression of a disease, or monitor responses to therapy, one or more organ-specific molecular blood fingerprints are detected/measured as described herein using any of the methods as described herein at one time point and detected/measured again at subsequent time points, thereby monitoring disease progression or responses to therapy.

The organ-specific molecular blood fingerprints of the present invention can be used to detect any of a variety of diseases (or the lack thereof). In certain embodiments, the organ-specific molecular blood fingerprints of the present invention can be used to detect cancer. As such, the present invention can be used to detect, monitor progression of, or monitor therapeutic regimens for any cancer, including melanoma, non-Hodgkin's lymphoma, Hodgkin's disease, leukemias, plasmocytomas, sarcomas, adenomas, gliomas, thymomas, breast cancer, prostate cancer, colo-rectal cancer, kidney cancer, renal cell carcinoma, bladder cancer, uterine cancer, pancreatic cancer, esophageal cancer, brain cancer, lung cancer, ovarian cancer, cervical cancer, testicular cancer, gastric cancer, multiple myeloma, hepatoma, acute lymphoblastic leukemia (ALL), acute myelogenous leukemia (AML), chronic myelogenous leukemia (CML), and chronic lymphocytic leukemia (CLL), or other cancers.

In certain embodiments, the organ-specific molecular blood fingerprints of the present invention can be used to detect, to monitor progression of, or monitor therapeutic regimens for diseases of the heart, kidney, ureter, bladder, urethra, liver, prostate, heart, blood vessels, bone marrow, skeletal muscle, smooth muscle, various specific regions of the brain (including, but not limited to the amygdala, caudate nucleus, cerebellum, corpus callosum, fetal, hypothalamus, thalamus), spinal cord, peripheral nerves, retina, nose, trachea, lungs, mouth, salivary gland, esophagus, stomach, small intestines, large intestines, hypothalamus, pituitary, thyroid, pancreas, adrenal glands, ovaries, oviducts, uterus, placenta, vagina, mammary glands, testes, seminal vesicles, penis, lymph nodes, thymus, and spleen. The present invention can be used to detect, to monitor progression of, or monitor therapeutic regimens for cardiovascular diseases, neurological diseases, metabolic diseases, respiratory diseases, autoimmune diseases. As would be recognized by the skilled artisan, the present invention can be used to detect, monitor the progression of, or monitor treatment for, virtually any disease wherein the disease causes perturbation in organ-specific secreted proteins.

In certain embodiments, the organ-specific molecular blood fingerprints of the present invention can be used to detect autoimmune disease. As such, the present invention can be used to detect, monitor progression of, or monitor therapeutic regimens for autoimmune diseases such as, but not limited to, rheumatoid arthritis, multiple sclerosis, insulin dependent diabetes, Addison's disease, celiac disease, chronic fatigue syndrome, inflammatory bowel disease, ulcerative colitis, Crohn's disease, Fibromyalgia, systemic lupus erythematosus, psoriasis, Sjogren's syndrome, hyperthyroidism/Graves disease, hypothyroidism/Hashimoto's disease, Insulin-dependent diabetes (type 1), Myasthenia Gravis, endometriosis, scleroderma, pernicious anemia, Goodpasture syndrome, Wegener's disease, glomerulonephritis, aplastic anemia, paroxysmal nocturnal hemoglobinuria, myelodysplastic syndrome, idiopathic thrombocytopenic purpura, autoimmune hemolytic anemia, Evan's syndrome, Factor VIII inhibitor syndrome, systemic vasculitis, dermatomyositis, polymyositis and rheumatic fever.

In certain embodiments, the organ-specific molecular blood fingerprints of the present invention can be used to detect diseases associated with infections with any of a variety of infectious organisms, such as viruses, bacteria, parasites and fungi. Infectious organisms may comprise viruses, (e.g., RNA viruses, DNA viruses, human in virus (HIV), hepatitis A, B, and C virus, herpes simplex virus (HSV), cytomegalovirus (CMV) Epstein-Barr virus (EBV), human papilloma virus (HPV)), parasites (e.g., protozoan and metazoan pathogens such as Plasmodia species, Leishmania species, Schistosoma species, Trypanosoma species), bacteria (e.g., Mycobacteria, in particular, M. tuberculosis, Salmonella, Streptococci, E. coli, Staphylococci), fungi (e.g., Candida species, Aspergillus species), Pneumocystis carinii, and prions.

Business Methods

A further embodiment of the present invention comprises a business method of diagnosing a particular disease in a subject that comprises detecting an organ-specific molecular blood fingerprint as described herein.

Thus, the present invention contemplates methods for (a) manufacturing one or more of the detection reagents, panels, arrays, (b) providing diagnostic services for determining organ-specific blood fingerprints, (c) providing manufacturers of genomics devices the use of the detection reagents, panels, arrays, blood fingerprints or transcriptomes described herein to develop diagnostic devices, where the genomics device includes any device that may be used to define differences in a blood sample between the normal and disturbed state (d) providing manufacturers of proteomics devices the use of the detection reagents, panels, arrays, blood fingerprints or transcriptomes described herein to develop diagnostic devices, where the proteomics device includes any device that may be used to define differences in a blood sample between the normal and disturbed state and (e) providing manufacturers of imaging devices the use of the detection reagents, panels, arrays, blood fingerprints or transcriptomes described herein to develop diagnostic devices, where the proteomics device includes any device that may be used to define differences in a blood sample between the normal and disturbed state (f) providing manufacturers of molecular imaging devices the use of the detection reagents, panels, arrays, blood fingerprints or transcriptomes described herein to develop diagnostic devices, where the proteomics device includes any device that may be used to define differences in a blood sample between the normal and disturbed state and (g) marketing to healthcare providers the benefits of using the detection reagents, panels, arrays, and diagnostic services of the present invention to enhance diagnostic capabilities and thus, to better treat patients.

Another aspect of the invention relates to a method for conducting a business, which includes: (a) manufacturing one or more of the detection reagents, panels, arrays, (b) providing diagnostic services for determining organ-specific molecular blood fingerprints and (c) marketing to healthcare providers the benefits of using the detection reagents, panels, arrays, and diagnostic services of the present invention to enhance diagnostic capabilities and thus, to better treat patients.

Another aspect of the invention relates to a method for conducting a business, comprising: (a) providing a distribution network for selling the detection reagents, panels, arrays, diagnostic services, and access to organ-specific molecular blood fingerprint databases (b) providing instruction material to physicians or other skilled artisans for using the detection reagents, panels, arrays, and organ-specific molecular blood fingerprint databases to improve diagnostics for patients.

Yet another aspect of the invention relates to a method for conducting a business, comprising: (a) identifying organ-specific secreted proteins in the blood sera, etc. (b) determining the organ-specific molecular fingerprint for any of a variety of diseases as described herein and (c) providing a distribution network for selling access to the database of organ-specific molecular fingerprints identified in step (b).

For instance, the subject business method can include an additional step of providing a sales group for marketing the database, or panels, or arrays, to healthcare providers.

Another aspect of the invention relates to a method for conducting a business, comprising: (a) determining one or more organ-specific molecular blood fingerprints and (b) licensing, to a third party, the rights for further development and sale of panels, arrays, and information databases related to the organ-specific molecular blood fingerprints of (a).

The business methods of the present application relate to the commercial and other uses, of the methodologies, panels, arrays, organ-specific secreted proteins, organ-specific molecular blood fingerprints, and databases comprising identified fingerprints of the present invention. In one aspect, the business method includes the marketing, sale, or licensing of the present invention in the context of providing consumers, i.e., patients, medical practitioners, medical service providers, and pharmaceutical distributors and manufacturers, with all aspects of the invention described herein, (e.g., the methods for identifying organ-specific secreted proteins, detection reagents for such proteins, molecular blood fingerprints, etc., as provided by the present invention).

In a particular embodiment of the present invention, a business method relating to providing information related to molecular blood fingerprints (e.g., levels of the plurality of organ-specific secreted proteins that make up a given fingerprint), method for determining fingerprints and sale of panels for determining such molecular blood fingerprints. In a specific embodiment, that method may be implemented through the computer systems of the present invention. For example, a user (e.g. a health practitioner such as a physician or a diagnostic laboratory technician) may access the computer systems of the present invention via a computer terminal and through the Internet or other means. The connection between the user and the computer system is preferably secure.

In practice, the user may input, for example, information relating to a patient such as the patient's disease state e.g., levels determined for the proteins that make up a given molecular blood fingerprint using a panel or array of the present invention. The computer system may then, through the use of the resident computer programs, provide a diagnosis that fits with the input information by matching the fingerprint parameters (e.g., levels of the proteins present in the blood as detected using a particular panel or array of the present invention) with a database of fingerprints.

A computer system in accordance with a preferred embodiment of the present invention may be, for example, an enhanced IBM AS/400 mid-range computer system. However, those skilled in the art will appreciate that the methods and apparatus of the present invention apply equally to any computer system, regardless of whether the computer system is a complicated multi-user computing apparatus or a single user device such as a personal computer or workstation. Computer systems suitably comprise a processor, main memory, a memory controller, an auxiliary storage interface, and a terminal interface, all of which are interconnected via a system bus. Note that various modifications, additions, or deletions may be made to the computer system within the scope of the present invention such as the addition of cache memory or other peripheral devices.

The processor performs computation and control functions of the computer system, and comprises a suitable central processing unit (CPU). The processor may comprise a single integrated circuit, such as a microprocessor, or may comprise any suitable number of integrated circuit devices and/or circuit boards working in cooperation to accomplish the functions of a processor.

In a preferred embodiment, the auxiliary storage interface allows the computer system to store and retrieve information from auxiliary storage devices, such as magnetic disk (e.g., hard disks or floppy diskettes) or optical storage devices (e.g., CD-ROM). One suitable storage device is a direct access storage device (DASD). A DASD may be a floppy disk drive that may read programs and data from a floppy disk. It is important to note that while the present invention has been (and will continue to be) described in the context of a fully functional computer system, those skilled in the art will appreciate that the mechanisms of the present invention are capable of being distributed as a program product in a variety of forms, and that the present invention applies equally regardless of the particular type of signal bearing media to actually carry out the distribution. Examples of signal bearing media include: recordable type media such as floppy disks and CD ROMS, and transmission type media such as digital and analog communication links, including wireless communication links.

The computer systems of the present invention may also comprise a memory controller, through use of a separate processor, which is responsible for moving requested information from the main memory and/or through the auxiliary storage interface to the main processor. While for the purposes of explanation, the memory controller is described as a separate entity, those skilled in the art understand that, in practice, portions of the function provided by the memory controller may actually reside in the circuitry associated with the main processor, main memory, and/or the auxiliary storage interface.

Furthermore, the computer systems of the present invention may comprise a terminal interface that allows system administrators and computer programmers to communicate with the computer system, normally through programmable workstations. It should be understood that the present invention applies equally to computer systems having multiple processors and multiple system buses. Similarly, although the system bus of the preferred embodiment is a typical hardwired, multidrop bus, any connection means that supports bidirectional communication in a computer-related environment could be used.

The main memory of the computer systems of the present invention suitably contains one or more computer programs relating to the organ-specific molecular blood fingerprints and an operating system. Computer program is used in its broadest sense, and includes any and all forms of computer programs, including source code, intermediate code, machine code, and any other representation of a computer program. The term “memory” as used herein refers to any storage location in the virtual memory space of the system. It should be understood that portions of the computer program and operating system may be loaded into an instruction cache for the main processor to execute, while other files may well be stored on magnetic or optical disk storage devices. In addition, it is to be understood that the main memory may comprise disparate memory locations.

All of the U.S. patents, U.S. patent application publications, U.S. patent applications, foreign patents, foreign patent applications and non-patent publications referred to in this specification and/or listed in the Application Data Sheet, are incorporated herein by reference, in their entirety. Moreover, all numerical ranges utilized herein explicitly include all integer values within the range and selection of specific numerical values within the range is contemplated depending on the particular use. Further, the following examples are offered by way of illustration, and not by way of limitation.

EXAMPLES Example 1 Evidence for the Presence of Disease-Perturbed Networks in Prostate Cancer Cells by Genomic and Proteomic Analyses: A Systems Approach to Disease

The following example demonstrates the presence of disease-perturbed networks in prostate.

Prostate cancer is the most common nondermatological cancer in the United States (Greenlee, R. T., et al., CA Cancer J Clin, 50: 7-33, 2000). Initially, its growth is androgen-dependent (AD); early-stage therapies, including chemical and surgical castration, kill cancerous cells by androgen deprivation. Although such therapies produce tumor regression, they eventually fail because most prostate carcinomas become androgen-independent (AI) (Isaacs, J. T. Urol Clin North Am, 26: 263-273, 1999). To improve the efficacy of prostate cancer therapy, it is necessary to understand the molecular mechanisms underlying the transition from androgen dependence to androgen independence.

The transition from AD to AI status likely results from multiple processes, including activation of oncogenes, inactivation of tumor suppressor genes, and changes in key components of signal transduction pathways and gene regulatory networks. Systems approaches to biology and disease are predicated on the identification of the elements of the systems, the delineation of their interactions and their changes in distinct disease states. Biological information is of two types: the digital information of the genome (e.g. genes and cis-control elements) and environmental cues. Proteins rarely act in isolation; rather, they form parts of molecular machines or participate in network interactions mediating cellular functions such as signal transduction and developmental or physiological response patterns. Gene regulatory networks, whose architecture and linkages are established by cis-control elements, integrate information from signal transduction networks and output it to developmental or physiological batteries or networks of effector proteins. Normal protein and gene regulatory networks may be perturbed by disease—through genetic and/or environmental perturbations and understanding these differences lies at the heart of systems approaches to disease. Disease-perturbed networks initiate altered responses that bring about pathologic phenotypes such as the invasiveness of cancer cells.

To map network perturbations in cancer initiation and progression, changes in expression levels of virtually all transcripts must be measured. Certain low-abundance transcripts, such as those encoding transcription factors and signal transducers, wield significant regulatory influences in spite of the fact they may be present in the cell at very low copy numbers. Differential display (Bussemakers, M. J., et al., Cancer Res, 59: 5975-5979, 1999) or cDNA microarrays (Vaarala, M. H., et al., Lab Invest, 80: 1259-1268, 2000; Chang, G. T., et al., Cancer Res, 57: 4075-4081, 1997) have been, used to profile changes in gene expression during the AD to AI transition; however, those technologies can identify only a limited number of more abundant mRNAs, and they miss many low-abundance mRNAs due to their low detection sensitivities. Massively parallel signature sequencing (MPSS), allows 20-nucleotide signature sequences to be determined in parallel for more than 1,000,000 DNA sequences (Brenner, et al., 2000, supra). MPSS technology allows identification and cataloging of almost all mRNAs that are changed between two cell states, even those with one or a few transcripts per cell, or between different organs or tissues. Differentially expressed genes thus identified can be mapped onto cellular networks to provide a systemic understanding of changes in cellular state.

Although transcriptome (mRNA levels) differences are easier to study than proteome (protein levels) differences and provide extremely valuable information, cellular functions are usually performed by proteins. RNA expression profiling studies do not address how the encoded proteins function biologically, and transcript abundance levels do not always correlate with protein abundance levels (Chen, G., et al., Mol Cell Proteomics, 1: 304-313, 2002). Therefore, the mRNA expression profiling described herein was complemented with a more limited protein profiling by using isotope-coded affinity tags (ICAT) coupled with tandem mass spectrometry (MS/MS) (Gygi, S. P., et al., Nat Biotechnol, 17: 994-999, 1999).

The LNCaP cell line is a widely used androgen-sensitive model for early-stage prostate cancer from which androgen-independent sublines have been generated (Vaarala, M. H., et al., 2000, supra; Chang, G. T., et al., 1997, supra; Patel, B. J., et al., J Urol, 164: 1420-1425, 2000). The cells of one such variant, CL-1, in contrast to their LNCaP progenitors, are highly tumorigenic, and exhibit invasive and metastatic characteristics in intact and castrated mice (Patel, G. J., et al., 2000, supra; Tso, C. L., et al., Cancer J Sci Am, 6: 220-233, 2000). Thus CL-1 cells model late-stage prostate cancer. MPSS and ICAT data extracted from these model cell lines can be validated by real-time RT-PCR or western blot analysis in more relevant biological models (tumor xenografts) and in tumor biopsies.

An MPSS analysis of about 5 million signatures was conducted for the androgen-dependent LNCaP cell line and its androgen-independent derivative CL1. The resulting database offers the first comprehensive view of the digital transcriptomes of prostate cancer cells and allows exploration of the cellular pathways perturbed during the transition from AD to AI growth. Additionally, protein expression profiles between LNCaP and CL1 cells were compared using ICAT/MS/MS technology. Further, computational analysis was used to identify those proteins that are secreted. Once such protein was further investigated and shown to be a diagnostic marker for prostate cancer used either alone, or in combination with the known PSA prostate cancer marker.

MPSS Analysis:

LNCaP and CL1 cells were grown using methods known in the art, for example, as described by Tso et al. 2000, supra). RNAs were isolated using Trizol (Life Technologies) according to the manufacturer's protocols (see, e.g., as described by Nelson et al. Proc Natl Acad Sci USA, 99: 11890-11895, 2002). MPSS cDNA libraries were constructed, individual cDNA sequences were amplified and attached to individual beads and sequenced as described by Brenner, et al., 2000, supra. The resulting signatures, generally 20 bases in length, were annotated using the then most recently annotated human genome sequence (human genome release hg16, released in November, 2003) and the human Unigene (Unigene build #184) according to a previously published method (Meyers, B. C., et al., Genome Res, 14: 1641-1653, 2004). Only 100% matches between an MPSS signature and a genome signature were considered. Those signatures that expressed at less than 3 tpm in both LNCaP and CL1 libraries were also excluded, as they might not be reliably detected (this represents less than one transcript per cell) (Jongeneel, C. V., et al., Proc Natl Acad Sci USA, 2003). Additionally, cDNA signatures were classified by their positions relative to polyadenylation signals and poly (A) tails and by their orientation relative to the 5′→3′ orientation of source mRNA. The Z-test (Man, M. Z., et al., Bioinformatics, 16: 953-959, 2000) was used to calculate P values for comparison of gene expression levels between the cell lines.

Isotope-Coded Affinity Tag (ICAT) Analysis:

ICAT reagents were purchased from Applied Biosystems Inc. Fractionation of cells into cytosolic, microsomal and nuclear fractions, as well as ICAT labeling, MS/MS, and data analyses were performed as described by Han et al. Nat Biotechnol, 19: 946-951, 2001. In addition, probability score analysis (Keller, A., et al., Anal Chem, 74: 5383-5392, 2002) and ASAPRatio (Automated Statistical Analysis on Protein Ratio) (Li, X. J., et al., Anal Chem, 75: 6648-6657, 2003) were used to assess the quality of MS spectra and to calculate protein ratios from multiple peptide ratios. (Briefly, and as described at http colon double slash regis dot systemsbiology dot net/software, Automated Statistical Analysis on Protein Ratio (ASAPRatio) accurately calculates the relative abundances of proteins and the corresponding confidence intervals from ICAT-type ESI-LC/MS data. The software first uses a Savitzky-Golay smoothing filter to reconstruct LC spectra of a peptide and its partner in a single charge state, subtracts background noise from each spectrum, and calculates light:heavy ratio of the peptide in that charge state. The ratios of the same peptide in different charge states are averaged and weighted by the corresponding spectrum intensity to obtain the peptide light:heavy ratio and its error. Subsequently, all unique peptides identified for a given protein are collected, their ratios and errors calculated, outliers are checked for using Dixon's tests, and the relative abundance and confidence interval for the protein are calculated by applying statistics for weighed samples. The software quickly generates a list of interesting proteins based on their relative abundance. A byproduct of the software is to identify outlier peptides which may be misidentified or, more interestingly, post-translationally modified.) To compare protein and mRNA expression levels, the Unigene numbers of the differentially expressed proteins were used to find MPSS signatures and their expression levels in transcripts per million (tpm). If one Unigene had more than one MPSS signature, likely due to alternative terminations, the average tpm of all signatures was taken.

Real-Time RT-PCR:

All primers were designed with the PRIMER3 program (httpcolon double slash www-genome dot wi dot mit dot edu slash cgi-bin slash primer slash primer3_www dot cgi) and BLAST-searched against the human cDNA and EST database for uniqueness. Real-time PCR was performed on an ABI 7700 machine (PE Biosystems) and the SYBR Green dye (Molecular Probe Inc.) was used as a reporter. PCR conditions were designed to give bands of the expected size with minimal primer dimer bands.

Identification of Perturbed Networks:

Genes in the 328 Biocarta and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways or networks (http colon double slash cgap dot nci dot nih dot gov slash Pathways slash) were downloaded and compared with the MPSS data, using Unigene IDs as identifiers. If a Unigene ID or an E.C. number corresponded to multiple signatures, potentially due to multiple alternatively terminated isoforms, the tpm counts of the isoforms were combined and then subjected to the Z-test (Man, M. Z., et al., 2000, supra). Genes with P values of 0.001 or less were considered to be significantly differentially expressed. The following criteria were used to identify perturbed networks: a perturbed network must have more than 3 genes represented our differentially expressed gene list (p<0.001) and at least 50% of those genes must be up regulated, it was considered an up-regulated pathway (vice versa for the down-regulated pathways).

Display of KEGG Networks by Cytoscape:

Cytoscape software was used (www dot cytoscape dot org) (Shannon, P., et al., Genome Res, 13: 2498-2504, 2003), to map the data onto the web of intracellular molecular interactions. We imported metabolic network maps and related information such as enzymes, substrates, and reactions from the recently developed KEGG (http colon double slash www dot genome dot ad dot jp slash) API 2.0 web server into the Cytoscape program. Expression data were thus automatically mapped to the KEGG and Biocarta pathways/networks and visualized by Cytoscape.

MPSS Analyses of the Androgen-Dependent LNCaP Cell Line and its Androgen-Independent Variant CL1:

Using MPSS technology, 2.22 million signature sequences were sequenced for LNCaP cells and 2.96 million for CL1 cells.

A total of 19,595 unique transcript signatures expressed at levels >3 tpm in at least one of the samples were identified. The signatures were classified into three major categories: 1093 signatures matched repeat sequences; 15,541 signatures matched unique cDNAs or ESTs, and 2961 signatures had no matches to any cDNA or EST sequences (but did match genomic sequences). The last category included sequences falling into one of three different categories: signatures representing new transcripts yet to be defined, signatures representing polymorphisms in cDNA sequences (a match of an MPSS sequence to cDNA or EST sequences requires 100% sequence identity), or errors in the MPSS reads. Transcript tags with matches to a cDNA or EST sequence were further classified based on the signatures' relative orientation to transcription direction and their position relative to a polyadenylation site and/or poly(A) tail. A searchable MySQL database (www dot mysql dot com) was also built containing the expression levels (tpm), the genomic locations of the MPSS sequences, the cDNAs or EST matches, and the classification of each signature.

The first analysis was restricted to those MPSS signatures corresponding to cDNAs with poly(A) tails and/or polyadenylation sites, so that corresponding genes could be conclusively identified. The Z-test was used to compare differential gene expression between LNCaP cells and CL1 cells (Mann, et al., 2000, supra). Using very stringent P values (less than 0.001), 2088 MPSS signatures were identified (corresponding to 1987 unique genes, as some genes have two or more MPSS signatures, due to alternative usages of polyadenylation sites) with significant differential expression. Of these, 1011 signatures (965 genes) were overexpressed in CL1 cells, and 1077 signatures (1022 genes) were overexpressed in LNCaP cells. The significance score of Z-test was dependent on the expression level. If a cut off P value of less than 0.001 was taken in the dataset, the expression level in tpm changed from 0 to 26 tpm for the most lowly expressed transcript (>26 fold); and changed from 7591 and 11206 tpm for the most highly expressed transcript (1.48 fold).

The expression levels of nine randomly chosen genes were identified using the MPSS and quantitative real-time RT-PCR techniques and showed that both RNA data sets were concordant. The MPSS expression profiling data were consistent with the available published data. For example, using RT-PCR, Patel et al. (Patel, B. J., et al., J Urol, 164: 1420-1425, 2000) showed that CL1 tumors express barely detectable prostate-specific antigen (PSA) and androgen receptor (AR) mRNAs as compared with LNCaP cells. The present MPSS results indicated that LNCaP cells expressed 584 tpm of androgen receptor (AR) and 841 tpm of PSA; CL1 cells did not express either AR or PSA (0 tpm in both cases). Freedland et al. found that CD10 expression was lost in CL1 cells compared with LNCaP cells (Freedland, S. J., et al., Prostate, 55: 71-80, 2003); the present study found that CD10 was expressed at 0 tpm in CL1 cells but at 56 tpm in LNCaP cells. Using cDNA microarrays, Vaarala et al. (Vaarala, M. H., et al., Lab Invest, 80: 1259-1268, 2000) compared LNCaP cells and another androgen-independent variant, non-PSA-producing LNCaP line, which is similar to CL1, and identified a total of 56 differentially expressed genes. We found completely concordant expression changes in these 56 genes between LNCaP and CL1 (in contrast to 1987 found by MPSS), and between LNCaP and non-PSA-producing LNCaP cells. This underscores the striking differences in sensitivity between the MPSS and cDNA microarray techniques.

CL1 cells do not express AR and thus lack the AR-mediated response program. To distinguish androgen response from other programs contributing to prostate cancer progression, the list of genes differentially expressed between LNCaP and CL1 cells were compared with a complementary list derived from MPSS analysis of LNCaP cells grown in the presence or absence of androgens (LNCaP R+/R−). From the 1987 differentially expressed gene between LNCaP and CL1, 525 genes were identified that were also differentially expressed in the LNCaP R+/R− dataset. Differential expression of these genes between LNCaP and CL1 cells probably reflects the fact that LNCaP cells express AR but CL1 does not, and the fact that normal medium contains some androgen. The remaining 1462 differentially expressed genes were not directly related to cellular AR status.

To compare the sensitivity of the MPSS and cDNA microarray procedures, cDNA microarrays containing 40,000 human cDNAs were hybridized to the same LNCaP and CL1 RNAs that were used for MPSS. Three replicate array hybridizations were performed. MPSS signatures and array clone IDs were mapped to Unigene IDs for data extraction and comparisons. The results showed that only those genes expressed at >40 tpm by MPSS could be reliably detected as changing levels by cDNA microarray hybridizations [judged by an expression level twice the standard deviation of the background, a standard cutoff value for microarray data analysis]. This observation is consistent with the 33-60 tpm sensitivity of microarrays estimated from the experiment performed by Hill et al. Science, 290: 809-812, 2000, in which known concentrations of synthetic transcripts were added. In LNCaP and CL1 cells, about 68.75% (13,471 of 19,595) of MPSS signatures (>3 tpm) were expressed at a level below 40 tpm; changes in the levels of these genes will be missed by microarray methods. Many attempts have been made to increase the sensitivity of DNA array technology (Han, M., et al., Nat Biotechnol, 19: 631-635, 2001; Bao, P., et al., Anal Chem, 74: 1792-1797, 2002.), however, the present study has not compared these new improvements against MPSS but it is clear that there will still be significant differences in the levels of change that can be detected.

SAGE (serial analysis of gene expression) (Velculescu, V. E., et al., Trends Genet, 16: 423-425, 2000) is another technology for gene expression profiling; like MPSS, it is digital and can generate a large number of signature sequences. However, MPSS, which can sequence ˜1 million signatures per sample, can achieve a much deeper coverage than SAGE (typical ˜10,000-100,000 signatures sequenced/sample) at reasonable cost. The MPSS data on LNCaP cells was compared against publicly available SAGE data on LNCaP cells (NCBI SAGE database) through common Unigene IDs. The SAGE library GSM724 (total SAGE tags sequenced: 22,721) (Lal, A., et al., Cancer Res, 59: 5403-5407, 1999) was derived from LNCaP cells with an inactivated PTEN gene; it is the SAGE library most similar to the LNCaP cells. Only 400 (about 20%) of the 1987 significantly differentially expressed genes (P<0.001) had any SAGE tag entry in GSM724. These data illustrate the importance of deep sequence coverage in identifying state changes in transcripts expressed at low abundance levels.

Functional Classifications of Genes Differentially Expressed Between LNCaP and CL1 Cells:

Examination of the GO (Gene Ontology) classification of the 1987 genes revealed that multiple cellular processes change during the transition from LNCaP cells to CL1 cells. The most interesting groups, categorized by function, are shown in Table 1.

Nineteen differentially expressed proteins are related to apoptosis. Twelve of these are up regulated in CL1 cells, including the apoptosis inhibitors Taxi (human T-cell leukemia virus type I) binding protein 1 (TAX1BP1) and CASP8 and FADD-like apoptosis regulator. Seven are down regulated in CL1, including programmed cell death 8 and 5 (apoptosis-inducing factors), and BCL2-like 13 (an apoptosis facilitator). Since CL1 cells have increased expression of apoptosis inhibitors and decreased expression of apoptosis inducers, net inhibition of apoptosis may contribute to their greater tumorigenicity.

TABLE 1 EXAMPLES OF DIFFERENTIALLY EXPRESSED GENES AND THEIR FUNCTIONAL CLASSIFICATIONS LNCaP CL1 Signatures (tpm) (tpm) Description GenBank ID SEQ ID NOS: Apoptosis related GATCAAATGTGTGGCCT 0 3609 lectin, BC001693 1574-1575 (SEQ ID NO: 3) galactoside- binding, soluble, 1 (galectin 1), GATCATAATGTTAACTA 0 14 pleiomorphic NM_002656 1576-1577 (SEQ ID NO: 4) adenoma gene- like 1 (PLAGL1) GATCATCCAGAGGAGCT 0 16 caspase 7, U40281 1578-1579 (SEQ ID NO: 5) apoptosis- related cysteine protease GATCGCGGTATTAAATC 0 15 tumor necrosis U75380 1580-1581 (SEQ ID NO: 6) factor receptor superfamily, member 12 GATCTCCTGTCCATCAG 0 24 interleukin 1, M15330 1582-1583 (SEQ ID NO: 7) beta GATCCCCTTCAAGGACA 1 19 nudix (nucleoside NM_006024 1584-1585 (SEQ ID NO: 8) diphosphate linked moiety X)-type motif 1 GATCATTGCCATCACCA 51 278 EST, Highly AL832733 1586 (SEQ ID NO: 9) similar to CUL2_HUMA N CULLIN HOMOLOG 2 GATCTGAAAATTCTTGG 16 56 CASP8 and U97075 r587-1588 (SEQ ID NO: 10) FADD-like apoptosis regulator GATCCACCTTGGCCTCC 49 149 tumor necrosis NM_003842 1589-1590 (SEQ ID NO: 11) factor receptor superfamily, member 10b GATCATGAATGACTGAC 118 257 cytochrome c BC009582 1591-1592 (SEQ ID NO: 12) GATCAAGTCCTTTGTGA 299 102 programmed H20713 1593 (SEQ ID NO: 13) cell death 8 (apoptosis- inducing factor) GATCACCAAAACCTGAT 72 24 BCL2-like 13 BM904887 1594 (SEQ ID NO: 14) (apoptosis facilitator) GATCAATCTGAACTATC 563 146 apoptosis NM_016085 1595-1596 (SEQ ID NO: 15) related protein APR-3 (APR-3) GATCCCTCTGTACAGGC 83 13 unc-13-like (C. NM_006377 1597-1598 (SEQ ID NO: 16) elegans) (UNC13), mRNA. GATCTGGTTGAAAATTG 1006 49 CED-6 protein NM_016315 1599-1600 (SEQ ID NO: 17) (CED-6), mRNA. GATCTCCCATGTTGGCT 86 4 CASP2 and BC017042 1601-1602 (SEQ ID NO: 18) RIPK1 domain containing adaptor with death domain GATCAGAAAATCCCTCT 27 1 DEAD/H (Asp- BC011556 1603-1604 (SEQ ID NO: 19) Glu-Ala- Asp/His) box polypeptide 20, 103 kDa GATCAAGGATGAAAGCT 50 3 programmed D20426 1605 (SEQ ID NO: 20) cell death 2 GATCTGATTATTTACTT 1227 321 programmed NM_004708 1606-1607 (SEQ ID NO: 21) cell death 5 GATCAAGTCCTTTGTGA 299 102 programmed NM_004208 1608-1609 (SEQ ID NO: 22) cell death 8 (apoptosis- inducing factor) Cyclins GATCCTGTCAAAATAGT 2 47 MCT-1 protein NM_014060 1610-1611 (SEQ ID NO: 23) (MCT-1), mRNA. GATCATTATATCATTGG 3 39 cyclin- NM_078487 1612-1613 (SEQ ID NO: 24) dependent kinase inhibitor 2B(CDKN2B) GATCATCAGTCACCGAA 38 396 cyclin- BM054921 1614 (SEQ ID NO: 25) dependent kinase inhibitor 2A (p16) GATCGGGGGCGTAGCAT 5 43 cyclin D1 NM_053056 1615-1616 (SEQ ID NO: 26) GATCTACTCTGTATGGG 40 144 cyclin fold BG119256 1617 (SEQ ID NO: 27) protein 1 GATCAGCACTCTACCAC 530 258 cyclin B1 BM973693 1618 (SEQ ID NO: 28) GATCTGGTGTAGTATAT 210 77 cyclin G2 BM984551 1619 (SEQ ID NO: 29) GATCAGTACACAATGAA 642 224 cyclin G1, BC000196 1620-1621 (SEQ ID NO: 30) GATCTCAGTTCTGCGTT 918 308 CDK2- NM_004642 1622-1623 (SEQ ID NO: 31) associated protein 1 (CDK2AP1), mRNA. GATCCTGAGCTCCCTTT 2490 650 cyclin I, BC000420 1624-1625 (SEQ ID NO: 32) GATCATGCAGTGACATA 15 1 KIAA1028 AL122055 1626-1627 (SEQ ID NO: 33) protein GATCTGTATGTGATTGG 28 1 cyclin M3 AA489077 1628 (SEQ ID NO: 34) Kallikreins GATCCACACTGAGAGAG 841 0 KLK3 AA523902 1629 (SEQ ID NO: 35) GATCCAGAAATAAAGTC 385 0 KLK4 AA595489 1630 (SEQ ID NO: 36) GATCCTCCTATGTTGTT 314 0 KLK2 S39329 1631-1633 (SEQ ID NO: 37) CD markers GATCAGAGAAGATGATA 0 810 CD213a2, U70981 1634-1635 (SEQ ID NO: 38) interleukin 13 receptor, alpha 2 GATCCCTAGGTCTTGGG 23 161 CD213a1, AW874023 1636 (SEQ ID NO: 39) interleukin 13 receptor, alpha 1 GATCCACATCCTCTACA 0 63 CD33,CD33 BC028152 1637-1638 (SEQ ID NO: 40) antigen (gp67) GATCAATAATAATGAGG 0 151 CD44,CD44 AL832642 1639-1640 (SEQ ID NO:41) antigen GATCCTTCAGCCTTCAG 0 35 CD73,5′- AI831695 1641 (SEQ ID NO: 42) nucleotidase, ecto (CD73) GATCTGGAACCTCAGCC 1 50 CD49e, BC008786 1642-1643 (SEQ ID NO: 43) integrin, alpha 5 GATCAGAGATGCACCAC 8 122 CD138, BM974052 1644 (SEQ ID NO: 44) syndecan 1 GATCAAAGGTTTAAAGT 38 189 CD166, AL833702 1645 (SEQ ID NO: 45) activated leukocyte cell adhesion molecule GATCAGCTGTTTGTCAT 53 295 CD71, BC001188 1646-1647 (SEQ ID NO: 46) transferrin receptor (p90, CD71) GATCGGTGCGTTCTCCT 287 509 CD107a, AI521424 1648 (SEQ ID NO: 47) lysosomal- associated membrane protein 1 GATCTACAAAGGCCATG 161 681 CD29, integrin, NM_002211 1649-1650 (SEQ ID NO: 48) beta 1 GATCATTTATTTTAAGC 56 0 CD10 (neutral BQ013520 1651 (SEQ ID NO: 49) endopeptidase, enkephalinase) GATCAGTCTTTATTAAT 150 50 CD107b, AI459107 1652 (SEQ ID NO: 50) lysosomal- associated membrane protein 2 GATCTTGGCTGTATTTA 84 1014 CD59 antigen NM_000611 1653-1654 (SEQ ID NO: 51) p18-20 GATCTTGTGCTGTGCTA 408 234 CD9 antigen NM_001769 1655-1656 (SEQ ID NO: 52) (p24) Transcription factors GATCAAATAACAAGTCT 0 62 transcription BM854818 1657 (SEQ ID NO: 53) factor BMAL2 GATCTCTATGTTTACIT 0 27 transcription BG163364 1658 (SEQ ID NO: 54) factor BMAL2 GATCCTGACACATAAGA 12 74 transcription BF055294 1659 (SEQ ID NO: 55) factor BMAL2 GATCATTTTGTATTAAT 10 61 transcription BC047878 1660-1661 (SEQ ID NO: 56) factor NRF GATCGTCTCATATTTGC 52 0 transcriptional NM_025085 1662-1663 (SEQ ID NO: 57) coactivator tubedown-100 GATCCCCCTCTTCAATG 0 31 transcriptional AJ299431 1664-1665 (SEQ ID NO: 58) co-activator with PDZ- binding motif GATCAAATGCTATTGCA 1 55 transcriptional AI126500 1666 (SEQ ID NO: 59) regulator interacting with the PHS- bromodomain 2 GATCTGTGACAGCAGCA 140 35 transducer of BC031406 1667-1668 (SEQ ID NO: 60) ERBB2, 1 GATCAAATCTGTACAGT 239 23 transducer of AA694240 1669 (SEQ ID NO: 61) ERBB2, 2 Annexins and their ligands GATCCTGTGCAACAAGA 0 69 annexin A10 BC007320 1670-1671 (SEQ ID NO: 62) GATCTGTGGTGGCAATG 41 630 annexin A11 AL576782 1672 (SEQ ID NO: 63) GATCAGAATCATGGTCT 0 1079 annexin A2 BC001388 1673-1674 (SEQ ID NO: 64) GATCTCTTTGACTGCTG 210 860 annexin A5 BC001429 1675-1676 (SEQ ID NO: 65) GATCCAAAAACATCCTG 83 241 annexin A6 AI566871 1677 (SEQ ID NO: 66) GATCAGAAGACTTTAAT 0 695 annexin Al BC001275 1678-1679 (SEQ ID NO: 67) GATCAGGACACTTAGCA 0 2949 S100 calcium BC015973 1680-1681 (SEQ ID NO:68) binding protein A10 (annexin II ligand) Matrix metalloproteinase GATCATCACAGTTTGAG 0 38 matrix BC002591 1682-1683 (SEQ ID NO: 69) metalloproteinase 10 (stromelysin 2) GATCCCAGAGAGCAGCT 0 108 matrix BC013118 1684-1685 (SEQ ID NO: 70) metalloproteinase 1 (interstitial collagenase) GATCGGCCATCAAGGGA 0 25 matrix AI370581 1686 (SEQ ID NO: 71) metalloproteinase 13 (collagenase 3) GATCTGGACCAGAGACA 0 10 matrix BG332150 1687 (SEQ ID NO: 72) metalloproteinase 2 (gelatinase A)

Matrix metalloproteinases (MMPs), which degrade extracellular matrix components that physically impede cell migration, are implicated in tumor cell growth, invasion, and metastasis. MMP1, 2, 10 and 13 were found to be significantly overexpressed in CL1 cells (Table 1), which may partially explain these cells' aggressive and metastatic behavior.

CD (cluster designation of monoclonal antibodies) markers are generally localized at the cell surface; some may be associated with prostate cancer (Liu, A. Y., et al., Prostate, 40: 192-199, 1999). All currently identified CD markers (CD1 to CD247) from the PROW CD index database (www dot ncbi dot nlm dot nih dot govslash prow slash guide slash 45277084 dot htm) were converted to UniGene numbers and the Unigene numbers used to identify their signatures and their expression levels. Fifteen CD markers were identified that were differentially expressed between LNCaP and CL1 cells (Z score <0.001) (Table 1). Eleven CD markers, including CD213a2 and CD213a1, which encode IL-13 receptors alpha 1 and 2, are up regulated in CL1 cells; three CD markers, CD9, CD10, and CD107, WERE downregulated in these cells (Table 1). Six CD markers went from 0 or 1 tpm to >35 tpm (Table 1), making them good digital or absolute markers or therapeutic targets. These data suggest that carefully selected CD markers may be useful in following the progression of prostate cancer, and indeed could serve as potential targets for antibody-mediated therapies (Liu, A. Y., et al., Prostate, 40: 192-199, 1999).

Delineation of Disease-Perturbed Networks in Prostate Cancer Cells.

Genes and proteins rarely act alone but rather generally operate in networks of interactions. Identifying key nodes (proteins) in the disease-perturbed networks may provide insights into effective drug targets. Comparing the genes (proteins) currently available in the 314 BioCarta and 155 KEGG pathway or network (http colon double slash cgap dot nci dot nih dot gov slash Pathways slash) databases with the MPSS data through Unigene IDs, we identified 37 BioCarta and 14 KEGG pathways that are up regulated and 23 BioCarta and 22 KEGG pathways down regulated in LNCaP cells versus CL1 cells (Table 2). The number of genes whose expression patterns changed in each pathway is listed in Table 2. Each gene along with its expression level in LNCaP and CL1 cells is listed pathway by pathway in our database (ftp colon double slash ftp dot systemsbiology dot net slash blin slash mpss). Changes in these pathways reveal the underlying phenotypic differences between LNCaP and CL1 cells. For example, multiple networks involved in modulating cell mobility, adhesion and spreading are up regulated in CL1 cells, which are more metastatic and invasive than LNCaP cells (Table 2). In the uCalpain and Friends in Cell Spread pathway, calpains are calcium-dependent thiol proteases implicated in cytoskeletal rearrangements and cell migration. During cell migration, calpain cleaves target proteins such as talin, ezrin, and paxillin at the leading edge of the membrane, while at the same time cleaving the cytoplasmic tails of the integrins β1(a) and β3(b) to release adhesion attachments at the trailing membrane edge. Increased activity of calpains increases migration rates and facilitates cell invasiveness (Liu, A. et al., Prostate, 40: 192-199, 1999).

TABLE 2 PATHWAYS THAT ARE UP OR DOWN REGULATED COMPARING LNCAP TO CL1 CELLS. # Genes hits # p < 0.001 & # p < 0.001 & # no Pathways in a pathway LNCA > CL1 LNCA < CL1 change Up-regulated Pathways in LNCAP cells BioCarta Pathways Mechanism of Gene Regulation 35 9 2 24 by Peroxisome Proliferators via PPARa alpha T Cell Receptor Signaling 21 6 2 13 Pathway ATM Signaling Pathway 15 5 2 8 CARM1 and Regulation of the 18 5 2 11 Estrogen Receptor HIV-I Nef negative effector of 33 5 2 26 Fas and TNF EGF Signaling Pathway 17 5 1 11 Role of BRCA1 BRCA2 and 16 5 1 10 ATR in Cancer Susceptibility TNFR1 Signaling Pathway 17 5 1 11 Toll-Like Receptor Pathway 17 5 1 11 FAS signaling pathway CD95 17 4 1 12 VEGF Hypoxia and 16 4 1 11 Angiogenesis Bone Remodelling 9 3 1 5 ER associated degradation 11 3 1 7 ERAD Pathway Estrogen-responsive protein 11 3 1 7 Efp controls cell cycle and breast tumors growth Influence of Ras and Rho 16 3 1 12 proteins on G1 to S Transition Inhibition of Cellular 13 3 1 9 Proliferation by Gleevec Map Kinase Inactivation of 9 3 1 5 SMRT Corepressor NFkB activation by 16 3 1 12 Nontypeable Hemophilus influenzae RB Tumor Suppressor 10 3 1 6 Checkpoint Signaling in response to DNA damage Transcription Regulation by 10 3 1 6 Methyltransferase of CARM1 Ceramide Signaling Pathway 13 4 0 9 Cystic fibrosis transmembrane 7 4 0 3 conductance regulator and beta 2 adrenergic receptor pathway Nerve growth factor pathway 11 4 NGF PDGF Signaling Pathway 16 4 0 12 TNF Stress Related Signaling 14 4 0 10 Activation of Csk by cAMP- 9 3 0 6 dependent Protein Kinase Inhibits Signaling through the T Cell Receptor AKAP95 role in mitosis and 11 3 0 8 chromosome dynamics Attenuation of GPCR Signaling 7 3 0 4 Chaperones modulate 11 3 0 8 interferon Signaling Pathway ChREBP regulation by 12 3 0 9 carbohydrates and cAMP IGF-1 Signaling Pathway 11 3 0 8 Insulin Signaling Pathway 11 3 0 8 NF-kB Signaling Pathway 11 3 0 8 Protein Kinase A at the 12 3 0 9 Centrosome Regulation of ck1 cdk5 by type 10 3 0 7 1 glutamate receptors Role of Mitochondria in 10 3 0 7 Apoptotic Signaling Signal transduction through 14 3 0 11 IL1R KEGG Pathways Aminosugars metabolism 24 9 4 11 Androgen and estrogen 37 13 5 19 metabolism Benzoate degradation via 5 3 1 hydroxylation C21-Steroid hormone 4 1 0 metabolism CS-Branched dibasic acid 2 2 0 0 metabolism Carbazole degradation 1 1 0 0 Terpenoid biosynthesis 6 4 1 1 Chondroitin_heparan sulfate 14 8 3 3 biosynthesis Fatty acid biosynthesis (path 1) 3 2 0 1 Fluorene degradation 3 2 0 Pentose and glucuronate 19 9 1 9 interconversions Phenylalanine, tyrosine and 10 5 2 3 tryptophan biosynthesis Porphyrin and chlorophyll 28 13 3 12 metabolism Streptomycin biosynthesis 6 4 1 1 Up-regulated Pathways in CL1 cells BioCarta Pathways Rho cell motility signaling 18 2 6 10 pathway Trefoil Factors Initiate 14 1 6 7 Mucosal Healing Integrin Signaling Pathway 14 1 5 8 Ca Calmodulin-dependent 7 1 4 2 Protein Kinase Activation Effects of calcineurin in 9 1 4 4 Keratinocyte Differentiation Angiotensin II mediated 12 1 3 8 activation of JNK Pathway via Pyk2 dependent signaling Bioactive Peptide Induced 16 1 3 12 Signaling Pathway CBL mediated ligand-induced 6 1 3 2 downregulation of EGF receptors Control of skeletal myogenesis 12 1 3 8 by HDAC calcium calmodulin-dependent kinase CaMK How does salmonella hijack a 8 1 3 4 cell Melanocyte Development and 4 1 3 0 Pigmentation Pathway Overview of telomerase protein 7 1 3 3 component gene hTert Transcriptional Regulation Regulation of PGC-1a 9 0 4 5 ADP-Ribosylation Factor 9 0 3 6 Downregulated of MTA-3 in 7 0 3 4 ER-negative Breast Tumors Endocytotic role of NDK 7 0 3 4 Phosphins and Dynamin Mechanism of Protein Import 7 0 3 4 into the Nucleus Nuclear Receptors in Lipid 7 0 3 4 Metabolism and Toxicity Pertussis toxin-insensitive 9 0 3 6 CCR5 Signaling in Macrophage Platelet Amyloid Precursor 5 0 3 2 Protein Pathway Role of Ran in mitotic spindle 8 0 3 5 regulation Sumoylation by RanBP2 8 0 3 5 Regulates Transcriptional Repression uCalpain and friends in Cell 5 0 3 2 spread KEGG Pathways Arginine and proline 45 7 16 22 metabolism ATP synthesis 31 7 15 9 Biotin metabolism 5 1 3 1 Blood group glycolipid 12 1 6 5 biosynthesis - lactoseries Cyanoamino acid metabolism 5 0 3 2 Ethylbenzene degradation 9 1 3 5 Ganglioside biosynthesis 16 2 6 8 Globoside metabolism 17 3 8 6 Glutathione metabolism 26 4 10 12 Glycine, serine and threonine 32 6 14 12 metabolism Glycosphingolipid metabolism 35 6 18 11 Glycosylphosphatidylinositol 26 5 12 9 (GPI)-anchor biosynthesis Glyoxylate and dicarboxylate 9 1 6 2 metabolism Huntington's disease 25 4 10 11 Methane metabolism 9 1 3 5 O-Glycans biosynthesis 19 3 8 8 One carbon pool by folate 12 2 8 2 Oxidative phosphorylation 93 21 45 27 Parkinson's disease 30 5 14 11 Phospholipid degradation 21 4 12 5 Synthesis and degradation of 7 1 3 3 ketone bodies Urea cycle and metabolism of 18 2 8 8 amino groups

Many pathways we identified as perturbed in the LNCaP and CL1 comparison are interconnected to form networks (in fact there are probably no discrete pathways, only networks). For example, the insulin signaling pathway, the signal transduction through IL1R pathway, NF-kB signaling pathway are interconnected through c-Jun, IL1R and NF-kB. The mapping of genes onto networks/pathways will be an ongoing objective as more networks/pathways become available. Our transcriptome data will be an invaluable resource in delineating these relationships.

As gene regulatory networks controlled by transcription factors form the top layer of the hierarchy that controls the physiological network, we sought to identify differentially expressed transcription factors. Of 554 transcription factors expressed in LNCaP and CL1 cells, 112 showed significantly different levels between the cell lines (P<0.001) This clearly demonstrated significant difference in the functioning of the corresponding gene regulatory networks during the progression of prostate cancer from the early to late stages.

Quantitative Proteomics Analysis of Prostate Cancer Cells.

We quantitatively profiled the protein expression changes between LNCaP and CL1 cells using the ICAT-MS/MS protocol described by Han et al. Nat Biotechnol, 19: 946-951, 2001. To increase proteome coverage, cells were separated into nuclear, cytosolic and microsomal fractions prior to ICAT analysis as described in Han et al., 2001, supra. We generated a total of 142,849 tandem mass spectra, 7282 of which corresponded to peptides with a mass spectrum quality score P value (Keller, A., et al., Anal Chem. 2002 Oct. 15; 74(20):5383-92) greater than 0.9 (allowing unambiguous identification of peptides). These 7282 peptides represented 971 proteins (Keller, A., et al., 2002, supra). We obtained quantitative peptide ratios for 4583 peptides corresponding to 941 proteins. The number of peptides is greater than the number of proteins because 1) mass spectrometry identified multiple peptides from the same protein and 2) the ionization step of mass spectrometry created different charge states for the same peptide. The protein ratios were calculated from multiple peptide ratios using an algorithm for the automated statistical analysis of protein abundance ratios (ASAPRatio) (Li, X. J., et al., Anal Chem, 75: 6648-6657, 2003). In the end, we identified 82 proteins that are down regulated and 108 proteins that are up regulated by at least 1.8-fold in LNCaP cells compared with CL1 cells. For example, five proteins belong to annexins that were markers for prostate and other cancers (Hayes, M. J. and Moss, S. E. Biochem Biophys Res Commun, 322: 1166-1170, 2004), seven are involved in fatty acids and lipid metabolism that are involved in the carcinogenesis and progression of prostate cancer (Pandian, S. S., et al., J R Coll Surg Edinb, 44: 352-361, 1999), five are related to apoptosis, 11 are cancer related, and five proteins are putative transcription factors. As we only identified a limited number of proteins that are significantly differentially expressed due to low sensitivity of ICAT technology, we were only able to identify a few pathways that are perturbed based on ICAT data alone (using the stringent criteria discussed above). This also illustrated the importance of MPSS analysis described earlier.

103 of 190 (54%) differentially expressed proteins identified have enzymatic activity and hence many are involved in metabolism. Notably, many of the proteins identified are involved in fatty acid and lipid metabolism, including fatty acid synthase, carnitine palmitoyltransferase II and propionyl Coenzyme A carboxylase alpha polypeptide. Fatty acid and lipid metabolism is known to be perturbed in prostate cancer (Fleshner, N., et al., J Urol, 171: S19-24, 2004). Additionally, many genes involved in lipid transport were altered, including the annexins, prosaposin, and fatty acid binding protein 5. Annexin A1 has previously been shown to be overexpressed in non-PSA-producing LNCaP cells as compared with PSA-producing LNCaP cells (Vaarala, M. H., et al., 2000, supra). Annexin A7 is postulated to be a prostate tumor suppressor gene (Cardo-Vila, M., et al., Pharmacogenomics J, 1: 92-94, 2001). Annexin A2 expression is reduced or lost in prostate cancer cells, and its re-expression inhibits prostate cancer cell migration (Liu, J. W., et al., Oncogene, 22: 1475-1485, 2003).

Other genes identified here have been implicated in carcinogenesis, including tumor suppressor p16 and insulin-like growth factor 2 receptor (Chi, S. G., et al., Clin Cancer Res, 3: 1889-1897, 1997; Kiess, W., et al., Horm Res, 41 Suppl 2: 66-73, 1994). Some genes have previously been implicated in prostate cancer, such as prostate cancer over expressed gene 1 POV1, which is over expressed in prostate cancer (Cole, K. A., et al., Genomics, 51: 282-287, 1998), and delta 1 and alpha 1 catenin (cadherin-associated protein) and junction plakoglobin, which are down regulated in prostate cancer cells (Kallakury, B. V., et al., Cancer, 92: 2786-2795, 2001). However, the potential relationships of most of the proteins identified here to prostate cancer require further elucidation. For example, transmembrane protein 4 (TMEM4), a gene predicted to encode a 182-amino acid type II transmembrane protein, is downregulated about twofold in CL1 cells compared with LNCaP cells. MPSS data also indicated that TMEM4 is down regulated about twofold in CL1 cells. Many type II transmembrane proteins, such as TMPRSS2, are overexpressed in prostate cancer patients (Vaarala, M. H., et al., Int. J Cancer, 94: 705-710, 2001). It will be interesting to see whether TMEM4 overexpression plays a primary role in prostate carcinogenesis. We also identified 12 proteins that have not been annotated or functionally characterized.

The mRNA expression level of eight proteins change from 0 tpm in LNCaP cells to greater than 50 tpm (we called them ‘digital changes’ because they go from zero to some expression) in CL1 cells, and that of one protein changed from 0 tpm in CL1 cells to greater than 50 in LNCaP cells. These genes can be used as digital diagnostic signals. Twenty-two of the differentially expressed proteins were predicted to be secreted proteins (See Table 3) and can be further evaluated as serum marker (see also Example 2 below).

Additionally, we sought to compare the expression at the protein level with that at the mRNA level. We converted the protein IDs and MPSS signatures to Unigene IDs to compare the MPSS data with the ICAT-MS/MS data. We limited this comparison to those with common Unigene IDs and with reliable ICAT ratios (standard deviation less than 0.5) and ended up with a subset of 79 proteins. Of these, 66 genes (83.5%) were concordant in their changes in mRNA and protein levels of expression and 13 genes (16.5%) were discordant, i.e. having higher protein expression but lower mRNA expression or vice versa. There are no functional similarities among the discordant genes. As these mRNAs and proteins are expressed at relatively high levels, discordance due to measurement errors is unlikely. Clearly posttranscriptional mechanism(s) of protein expression are functioning, although the elucidation of the specific mechanism(s) awaits further studies.

Thus, these results, and those described in the Examples below, indicate a systems approach to disease will offer powerful tools for diagnostics, therapeutics, and even aid in prevention in the future.

TABLE 3 DIFFERENTIALLY EXPRESSED GENES THAT ENCODE PREDICTED SECRETED PROTEINS. SEQ Accession SEQ ID Signature ID NO: Number NOS: Description GATCAGCATGGGCCACG  73 NM_001928 594-595 D component of complement (adipsin) GATCTACTACTTGGCCT  74 NM_006280 596-597 signal sequence receptor, delta (translocon- associated protein delta) GATCCTGTTGGGAAAGA  75 NM_203329 598-599 CD59 antigen p18-20 (antigen identified by monoclonal antibodies 16.3A5, EJ16, EJ30, EL32 and G344) GATCCTGTTGGGAAAGA  76 NM_203331 600-601 CD59 antigen p18-20 (antigen identified by monoclonal antibodies 16.3A5, EJ16, EJ30, EL32 and G344) GATCCCTGAAGTTGCCC  77 NM_203331 600-601 CD59 antigen p18-20 (antigen identified by monoclonal antibodies 16.3A5, EJ16, EJ30, EL32 and G344) GATCTTGGCTGTATTTA  78 NM_203331 600-601 CD59 antigen p18-20 (antigen identified by monoclonal antibodies 16.3A5, EJ16, EJ30, EL32 and G344) GATCCCTGAAGTTGCCC  79 NM_203330 602-603 CD59 antigen p18-20 (antigen identified by monoclonal antibodies 16.3A5, EJ16, EJ30, EL32 and G344) GATCCTGTTGGGAAAGA  80 NM_203330 602-603 CD59 antigen p18-20 (antigen identified by monoclonal antibodies 16.3A5, EJ16, EJ30, EL32 and G344) GATCTTGGCTGTATTTA  81 NM_203330 602-603 CD59 antigen p18-20 (antigen identified by monoclonal antibodies 16.3A5, EJ16, EJ30, EL32 and G344) GATCCCTGAAGTTGCCC  82 NM_203329 598-599 CD59 antigen p18-20 (antigen identified by monoclonal antibodies 16.3A5, EJ16, EJ30, EL32 and G344) GATCTTGGCTGTATTTA  83 NM_000611 604-605 CD59 antigen p18-20 (antigen identified by monoclonal antibodies 16.3A5, EJ16, EJ30, EL32 and G344) GATCCCTGAAGTTGCCC  84 NM_000611 604-605 CD59 antigen p18-20 (antigen identified by monoclonal antibodies 16.3A5, EJ16, EJ30, EL32 and G344) GATCCTGTTGGGAAAGA  85 NM_000611 604-605 CD59 antigen p18-20 (antigen identified by monoclonal antibodies 16.3A5, EJ16, EJ30, EL32 and G344) GATCTTGGCTGTATTTA  86 NM_203329 598-599 CD59 antigen p18-20 (antigen identified by monoclonal antibodies 16.3A5, EJ16, EJ30, EL32 and G344) GATCTGTGCTGACCCCA  87 NM_002982 606-607 chemokine (C-C motif) ligand 2 GATCTCTTGGAATGACA  88 NM_012242 608-609 dickkopf homolog 1 (Xenopus laevis) GATCACCATCAAGCCAG  89 NM_012242 608-609 dickkopf homolog 1 (Xenopus laevis) GATCAAACAGCTCTAGT  90 NM_016308 610-611 UMP-CMP kinase GATCCCCTGTTACGACA  91 NM_014155 612-613 HSPC063 protein GATCTCTGATTACCAGC  92 NM_025205 614-615 mediator of RNA polymerase II transcription, subunit 28 homolog (yeast) GATCATTGAACGAGACA  93 NM_031903 616-617 mitochondrial ribosomal protein L32 GATCACAGACCACGAGT  94 NM_178507 618-619 NS5ATP13TP2 protein GATCTGCATCAGTTGTA  95 NM_148170 620-621 cathepsin C GATCTCTTGCTAGATTT  96 NM_005059 622-623 relaxin 2 GATCACAAGGCTGCCTG  97 NM_000405 624-625 GM2 ganglioside activator GATCGTTTCTCATCTCT  98 NM_006432 626-627 Niemann-Pick disease, type C2 GATCCCCGCGATACTTC  99 NM_015921 628-629 chromosome 6 open reading frame 82 GATCTTTTTTTGGATAT 100 NM_181777 630-631 ubiquitin-conjugating enzyme E2A (RAD6 homolog) GATCCGAGAGTAAGGAA 101 NM_032488 632-633 cornifelin GATCATGTGTTTCCATG 102 NM_014435 634-635 N-acylsphingosine amidohydrolase (acid ceramidase)-like GATCTCAGAACAACCTT 103 NM_016029 636-637 dehydrogenase/ reductase (SDR family) member 7 GATCTTACCTCCTGATA 104 NM_020467 638-639 hypothetical protein from clone 643 GATCCCAGACTGGTTCT 105 NM_003782 640-641 UDP-Gal:betaGlcNAc beta 1,3- galactosyltransferase, polypeptide 4 GATCAAGTGCATTTGAC 106 NM_173631 642-643 zinc finger protein 547 GATCAGTGCGTCATGGA 107 NM_005423 644-645 trefoil factor 2 (spasmolytic protein 1) GATCCAAGAGGAAGAAT 108 NM_014402 646-647 low molecular mass ubiquinone-binding protein (9.5kD) GATCCAGCAAACAGGTT 109 NM_003851 648-649 cellular repressor of E1A-stimulated genes 1 GATCATAGAAGGCTATT 110 NM_181834 650-651 neurofibromin 2 (bilateral acoustic neuroma) GATCCCCCTTCATTTGA 111 NM_004862 652-653 lipopolysaccharide- induced TNF factor GATCCCAAATTTGAAGT 112 NM_001685 654-655 ATP synthase, H+ transporting, mitochondrial F0 complex, subunit F6 GATCTGCTTTCTGTAAT 113 NM_002406 656-657 mannosyl (alpha-1,3-)- glycoptein beta-1,2-N- acetylglucosaminyltransferase GATCACTCCTTATTTGC 114 NM_019021 658-659 hypothetical protein FLJ20010 GATCACCTTCGACGACT 115 NM_003130 660-661 sorcin GATCTCTATTGTAATCT 116 NM_002489 662-663 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 4, 9 kDa GATCTCCTGGCTGCAAA 117 NM_138429 664-665 claudin 15 GATCCCAGTCTCTGCCA 118 NM_201397 666-667 glutathione peroxidase 1 GATCTTCTTTATAATTC 119 NM_004048 668-669 beta-2-microglobulin GATCTGTTCAAACAGCA 120 NM_024060 670-671 hypothetical protein MGC5395 GATCGTGCTCACAGGCA 121 NM_033280 672-673 SEC11-like 3 (S. cerevisiae) GATCAATATGTAAATAT 122 NM_020199 674-675 chromosome 5 open reading frame 15 GATCAGCTTTGCTCCTG 123 NM_207495 676-677 hypothetical protein DKFZp686I15217 GATCTCTATGGCTGTAA 124 NM_033211 678-679 hypothetical gene supported by AF038182; BC009203 GATCTCAGAACCTCTGT 125 NM_00100143 680-681 similar to RIKEN cDNA 4921524J17 GATCCAGCCATTACTAA 126 NM_016205 682-683 platelet derived growth factor C GATCTTTCCCAAGATTG 127 NM_001001434 684-685 syntaxin 16 GATCGATTCTGTGACAC 128 NM_181726 686-687 low density lipoprotein receptor-related protein binding protein GATCTATTTTTTCTAAA 129 NM_004125 688-689 guanine nucleotide binding protein (G protein), gamma 10 GATCAAGAATCCTGCTC 130 NM_006332 690-691 interferon, gamma- inducible protein 30 GATCGGTGGAGAACCTC 131 NM_175742 692-693 melanoma antigen, family A, 2 GATCGGTGGAGAACCTC 132 NM_175743 694-695 melanoma antigen, family A, 2 GATCGGTGGAGAACCTC 133 NM_153488 696-697 melanoma antigen, family A, 2B GATCATGGGTGAGGGGT 134 NM_001483 698-699 glioblastoma amplified sequence GATCCCCCTCACCATGA 135 NM_032621 700-701 brain expressed X-linked 2 GATCAACTAATAGCTCT 136 NM_181892 702-703 ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homolog, yeast) GATCAAATAAAGTTATA 137 NM_181892 702-703 ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homolog, yeast) GATCAAGGAGACCCGGA 138 NM_024540 704-705 mitochondrial ribosomal protein L24 GATCAAGGAGACCCGGA 139 NM_145729 706-707 mitochondrial ribosomal protein L24 GATCCTAAGCCATAGAC 140 NM_025075 708-709 Ngg1 interacting factor 3 like 1 binding protein 1 GATCCATTGAGCCCAGC 141 NM_181725 710-711 hypothetical protein FLJ12760 GATCTGAGGGCGTCTTC 142 NM_012153 712-713 ets homologous factor GATCTCGGTAGTTACGT 143 NM_012153 712-713 ets homologous factor GATCCCAAGATGATTAA 144 NM_014177 714-715 chromosome 18 open reading frame 55 GATCTCAAACTTGTCTT 145 NM_003350 716-717 ubiquitin-conjugating enzyme E2 variant 2 GATCATAGTTATTATAC 146 NM_032466 718-719 aspartate beta- hydroxylase GATCCCAACTGCTCCTG 147 NM_005947 720-721 metallothionein 1B (functional) GATCAAAATGCTAAAAC 148 NM_016311 722-723 ATPase inhibitory factor 1 GATCTGITTGTTCCCTG 149 NM_013411 724-725 adenylate kinase 2 GATCAACAGTGGCAATG 150 NM_001001392 726-727 CD44 antigen (homing function and Indian blood group system) GATCAATAATAATGAGG 151 NM_001001392 726-727 CD44 antigen (homing function and Indian blood group system) GATCAACTAATAGCTCT 152 NM_181890 728-729 ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homolog, yeast) GATCAAATAAAGTTATA 153 NM_181891 730-731 ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homolog, yeast) GATCAAATAAAGTTATA 154 NM_181890 728-729 ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homolog, yeast) GATCAAATAAAGTTATA 155 NM_181889 732-733 ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homolog, yeast) GATCAACTAATAGCTCT 156 NM_003340 734-735 ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homolog, yeast) GATCAACTAATAGCTCT 157 NM_181888 736-737 ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homolog, yeast) GATCAAATAAAGTTATA 158 NM_181888 736-737 ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homolog, yeast) GATCAACTAATAGCTCT 159 NM_181891 730-731 ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homolog, yeast) GATCAACTAATAGCTCT 160 NM_181887 738-739 ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homolog, yeast) GATCAAATAAAGTTATA 161 NM__181887 738-739 ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homolog, yeast) GATCAACTAATAGCTCT 162 NM_181886 740-741 ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homolog, yeast) GATCAAATAAAGTTATA 163 NM_181886 740-741 ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homolog, yeast) GATCAAATAAAGTTATA 164 NM_003340 734-735 ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homolog, yeast) GATCAACTAATAGCTCT 165 NM_181889 732-733 ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homolog, yeast) GATCTGATTTTTTCCCC 166 NM_145751 742-743 TN Freceptor-associated factor 4 GATCAGAAATGACTGTG 167 NM_018509 744-745 hypothetical protein PRO1855 GATCACTGAGAAAAAAT 168 NM_152407 746-747 GrpE-like 2, mitochondrial (E. coli) GATCCAAGAGTTTAGTG 169 NM_006807 748-749 chromobox homolog 1 (HP1 beta homolog Drosophila ) GATCTTTGCTGGCAAGC 170 NM_002954 750-751 ribosomal protein S27a GATCCACACTGAGAGAG 171 NM_145864 752-753 kallikrein 3, (prostate specific antigen) GATCTGTATTATTAAAT 172 NM_032549 754-755 IMP2 inner mitochondrial membrane protease-like (S. cerevisiae) GATCTGTTTGTTCCCTG 173 NM_172199 756-757 adenylate kinase 2 GATCCCCTGCCTGGTGC 174 NM_001312 758-759 cysteine-rich protein 2 GATCAACTAATAGCTCT 175 NM_181893 760-761 ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homolog, yeast) GATCAAATAAAGTTATA 176 NM_181893 760-761 ubiquitin-conjugating enzyme E2D 3 (UBC4/5 homolog, yeast) GATCTTTTTCAAGTCTT 177 NM_012071 762-763 COMM domain containing 3 GATCATGTATGAGATAG 178 NM_012460 764-765 translocase of inner mitochondrial membrane 9 homolog (yeast) GATCCTTCAGGCAGTAA 179 NM_176805 766-767 mitochondrial ribosomal protein S11 GATCITTTTITGGATAT 180 NM_003336 768-769 ubiquitin-conjugating enzyme E2A (RAD6 homolog) GATCCCAGTCTCTGCCA 181 NM_000581 770-771 glutathione peroxidase 1 GATCAAGACGAGCCTGC 182 NM_004864 772-773 growth differentiation factor 15 GATCCCAGCTGATGTAG 183 NM_001885 774-775 crystallin, alpha B GATCATGAAGACCTGCT 184 NM_003754 776-777 eukaryotic translation initiation factor 3, subunit 5 epsilon, 47 kDa GATCTCAAGGTTGATAG 185 NM_003864 778-779 sin3-associated polypeptide, 30 kDa GATCACCAGGCTGCCCA 186 NM_148571 780-781 mitochondrial ribosomal protein L27 GATCAAAATGCTAAAAC 187 NM_178190 782-783 ATPase inhibitory factor 1 GATCAAGATGACACTGA 188 NM_004483 784-785 glycine cleavage system protein H (aminomethyl carrier) GATCGGGAACTCCTGCT 189 NM_005952 786-787 metallothionein 1X GATCTTGTCTTTAAAAC 190 NM_015646 788-789 RAP1B, member of RAS oncogene family GATCCACACACGTTGGT 191 NM_003255 790-791 tissue inhibitor of metalloproteinase 2 GATCATCAGTCACCGAA 192 NM_000077 792-793 cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) GATCCAGTATTCAGTCA 193 NM_002166 794-795 inhibitor of DNA binding 2, dominant negative helix-loop-helix protein GATCCTTGCAGGGAGCT 194 NM_015343 796-797 dullard homolog (Xenopus laevis) GATCTCCTTGCCCCAGC 195 NM_015343 796-797 dullard homolog (Xenopus laevis) GATCGCCTAGTATGTTC 196 NM_003897 798-799 immediate early response 3 GATCAGACTGTATTAAA 197 NM_032052 800-801 zinc finger protein 278 GATCGGCCCTACTAGAT 198 NM_032052 800-801 zinc finger protein 278 GATCTCCCACTGCGGGG 199 NM_032052 800-801 zinc finger protein 278 GATCTGTGATGGTCAGC 200 NM_000232 802-803 sarcoglycan, beta (43 kDa dystrophin-associated glycoprotein) GATCACTGTGGTATCTA 201 NM_052822 804-805 secretory carrier membrane protein 1 GATCATCAGTCACCGAA 202 NM_058197 806-807 cyclin-dependent kinase inhibitor 2A (melanoma, p16, inhibits CDK4) GATCATTTGTTTATTAA 203 NM_022334 808-809 integrin beta 1 binding protein 1 GATCAAATATGTAAAAT 204 NM_004842 810-811 A kinase (PRKA) anchor protein 7 GATCTCTTGCTAGATTT 205 NM_134441 812-813 relaxin 2 GATCACCTTCGACGACT 206 NM_198901 814-815 sorcin GATCGGATTGATTAAAA 207 NM_020353 816-817 phospholipid scramblase 4 GATCTAGTTGGGAGATA 208 NM_153367 818-819 chromosome 10 open reading frame 56 GATCTTTTTTGGCTACT 209 NM_018424 820-821 erythrocyte membrane protein band 4.1 like 4B GATCACATTTTCTGTTG 210 NM_201436 822-823 H2A histone family, member V GATCACCTGGGTTTCTT 211 NM_021999 824-825 integral membrane protein 2B GATCTATTAGATTCAAA 212 NM_021105 826-827 phospholipid scramblase 1 GATCTCTTATTTTACAA 213 NM_000546 828-829 tumor protein p53 (Li- Fraumeni syndrome) GATCATAGAAGGCTATT 214 NM_181835 830-831 neurofibromin 2 (bilateral acoustic neuroma) GATCTTCCTGGACAGGA 215 NM_152992 832-833 POM (POM121 homolog, rat) and ZP3 fusion GATCAAGGACCGGCCCA 216 NM_032391 834-835 small nuclear protein PRAC GATCGCATTTTTGTAAA 217 NM_058171 836-837 inhibitor of growth family, member 2 GATCCATCCTCATCTCC 218 NM_020188 838-839 DC13 protein GATCGATGGTGGCGCTT 219 NM_138992 beta-site APP-cleaving enzyme 2 GATCTTATAAAAAGAAA 220 NM_017998 840-841 chromosome 9 open reading frame 40 GATCTGAACGATGCCGT 221 NM_024579 842-843 hypothetical protein FLJ23221 GATCTCCCCGCCGCAGC 222 NM_015973 844-845 galanin GATCGTCGTCCAGGCCA 223 NM_032920 846-847 chromosome 21 open reading frame 124 GATCGTTGGGGAACCCC 224 NM_199483 848-849 chromosome 20 open reading frame 24 GATCCTATATGTCCTGT 225 NM_152344 850-851 hypothetical protein FLJ30656 GATCGATGGTTGACAAT 226 NM_004552 852-853 NADH dehydrogenase (ubiquinone) Fe—S protein 5, 15 kDa (NADH-coenzyme Q reductase) GATCTTGTACTAACTTA 227 NM_019059 854-855 translocase of outer mitochondrial membrane 7 homolog (yeast) GATCCCGATGTTCTTAA 228 NM_001806 856-857 CCAAT/enhancer binding protein (C/EBP), gamma GATCCTGTTTAACAAAG 229 NM_015469 858-859 nipsnap homolog 3A (C. elegans) GATCACGCACACACAAT 230 NM_198337 860-861 insulin induced gene 1 GATCCAGCCAGACTTGC 231 NM_144772 862-863 apolipoprotein A—I binding protein GATCCACACTGGAGAGA 232 NM_003450 864-865 zinc finger protein 174 GATCTCAGTTCTGCGTT 233 NM_004642 866-867 CDK2-associated protein 1 GATCTACACCTCTTGCC 234 NM_052845 868-869 methylmalonic aciduria (cobalamin deficiency) type B GATCCAGCTGGAAAGCT 235 NM_006406 870-871 peroxiredoxin 4 GATCCTTCAGGCAGTAA 236 NM_022839 872-873 mitochondrial ribosomal protein S11 GATCCACACTGAGAGAG 237 NM_001648 874-875 kallikrein 3, (prostate specific antigen) GATCACCTTATGGATGT 238 NM_003932 876-877 suppression of tumorigenicity 13 (colon carcinoma) (Hsp70 interacting protein) GATCTAGTTATTTTAAT 239 NM_172178 878-879 mitochondria ribosomal protein L42 GATCATTGAGAATGCAG 240 NM_206966 880-881 similar to AVLV472 GATCATGCCAAGTGGTG 241 NM_058248 882-883 deoxyribonuclease II beta GATCACATTTTCTGTTG 242 NM_201516 884-885 H2A histone family, member V GATCAGAAAGAAACCTT 243 NM_006744 886-887 retinol binding protein 4, plasma GATCCGTGGCAGGGCTG 244 NM_031901 888-889 mitochondrial ribosomal protein S21 GATCCGTGGCAGGGCTG 245 NM_018997 890-891 mitochondrial ribosomal protein S21 GATCTATCACCCAAACA 246 NM_198157 892-893 ubiquitin-conjugating enzyme E2L 3 GATCAAGCGTGCTTTCC 247 NM_000995 894-895 ribosomal protein L34 GATCAAGCGTGCTTTCC 248 NM_033625 896-897 ribosomal protein L34 GATCCCTCATCCCTGAA 249 NM_014098 898-899 peroxiredoxin 3 GATCCACCTTGGCCTCC 250 NM_147187 900-901 tumor necrosis factor receptor superfamily, member 10b GATCTTAGGGAGACAAA 251 NM_182529 902-903 TRAP domain containing 5 GATCAAGATACGGAAGA 252 NM_177924 904-905 N-acylsphingosine amidohydrolase (acid ceramidase) 1 GATCTGTTTGTTCCCTG 253 NM_001625 906-907 adenylate kinase 2 GATCAGCAAAAGCCAAA 254 NM_201263 908-909 tryptophanyl tRNA synthetase 2 (mitochondrial) GATCGGGGGAGGGTAAA 255 NM_004544 910-911 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 10, 42 kDa GATCGTGGAGGAGGGAC 256 NM_016310 912-913 polymerase (RNA) III (DNA directed) polypeptide K, 12.3 kDa GATCACTTTTGAAAGCA 257 NM_018465 914-915 chromosome 9 open reading frame 46 GATCTGATTTGCTAGTT 258 NM_015147 916-917 KIAA0582 GATCCTAGGGGGTTTTG 259 NM_015147 916-917 KIAA0582 GATCTAAGTTGCCTACC 260 NM_014176 918-919 HSPC150 protein similar to ubiquitin-conjugating enzyme GATCTTTGTTCTTGACC 261 NM_020531 920-921 chromosome 20 open reading frame 3 GATCTCTTAGCCAGAGG 262 NM_153333 922-923 transcription elongation factor A (SII)-like 8 GATCTCTCTCACCTACA 263 NM_003287 924-925 tumor protein D52-like 1 GATCAGAGGTGAAGGGA 264 NM_007021 926-927 chromosome 10 open reading frame 10 GATCTCATTGATGTACA 265 NM_032947 928-929 putative small membrane protein NID67 GATCTGTGCCGGCTTCC 266 NM_005656 930-931 transmembrane protease, serine 2 GATCCGTCTGTGCACAT 267 NM_005656 930-931 transmembrane protease, serine 2 GATCGGCTCTGGGAGAC 268 NM_006315 932-933 ring finger protein 3 GATCGATTAATGAAGTG 269 NM_016326 934-935 chemokine-like factor GATCCTGGACTGGGTAC 270 NM_006830 936-937 ubiquinol-cytochrome c reductase (6.4 kD) subunit GATCTTGGAGAATGTGA 271 NM_001216 938-939 carbonic anhydrase IX GATCTTTTTTTGGATAT 272 NM_181762 940-941 ubiquitin-conjugating enzyme E2A (RAD6 hoinolog) GATCTAGTTATTTTAAT 273 NM_014050 942-943 mitochondrial ribosomal protein L42 GATCTAGTTATTTTAAT 274 NM_172177 944-945 mitochondrial ribosomal protein L42 GATCAAGGGACGGCTGA 275 NM_000978 946-947 ribosomal protein L23 GATCAGAAGGCTCTGGT 276 NM_018442 948-949 IQ motif and WD repeats 1 GATCAATGTTGAAGAAT 277 NM_018442 948-949 IQ motif and WD repeats 1 GATCCTGCACTCTAACA 278 NM_203339 950-951 clusterin (complement lysis inhibitor, SP-40,40, sulfated glycoprotein 2, testosterone-repressed prostate message 2, apolipoprotein J) GATCTGATTATTTACTI 279 NM_004708 952-953 programmed cell death 5 GATCCTTGAAGGCAGCT 280 NM_197958 954-955 acheron GATCCCTTTTCTTACTA 281 NM_153713 956-957 hypothetical protein MGC46719 GATCTGTCCACTTCTGG 282 NM_153713 956-957 hypothetical protein MGC46719 GATCAGATACCACCAAG 283 NM_001001503 958-959 NADH dehydrogenase (ubiquinone) flavoprotein 3, 10 kDa GATCCTTTGGATTAATC 284 NM_016138 960-961 coenzyme Q7 homolog, ubiquinone (yeast) GATCATTATTTCTGTCT 285 NM_018184 962-963 ADP-ribosylation factor- like 10C GATCAGCCCTCAAAGAA 286 NM_018184 962-963 ADP-ribosylation factor- like 10C GATCAGCAAAAATAAAG 287 NM_016096 964-965 HSPC038 protein GATCTCAGCGGCATTAA 288 NM_052951 966-967 deoxynucleotidyltransferase, terminal, interacting protein 1 GATCCCTGGAGTGCCTT 289 NM_003226 968-969 trefoil factor 3 (intestinal) GATCTGTTTCTACCAAT 290 NM_183045 970-971 ring finger protein (C3H2C3 type) 6 GATCCTGCTGTGAAAGG 291 NM_153750 972-973 chromosome 21 open reading frame 81 GATCTTGAAAGTGCCTG 292 NM_022130 974-975 golgi phosphoprotein 3 (coat-protein) GATCAATACAATAACAA 293 NM_003479 976-977 protein tyrosine phosphatase type IVA, member 2 GATCTCCTATGAGAACA 294 NM_003479 976-977 protein tyrosine phosphatase type IVA, member 2 GATCAATACAATAACAA 295 NM_080391 978-979 protein tyrosine phosphatase type IVA, member 2 GATCTCCTATGAGAACA 296 NM_080391 978-979 protein tyrosine phosphatase type IVA, member 2 GATCCAACCCTGTACTG 297 NM_177969 980-981 protein phosphatase 1B (formerly 2C), magnesium-dependent, beta isoform GATCTCTACCATTTAAT 298 NM_001017 982-983 ribosomal protein S13 GATCCAGAAATACTTAA 299 NM_005410 984-985 selenoprotein P, plasma, 1 GATCCAATGCTAAACTC 300 NM_005410 984-985 selenoprotein P, plasma, 1 GATCAAATGAGAATAAA 301 NM_182620 986-987 family with sequence similarity 33, member A GATCCTTGCCACAAGAA 302 NM_004034 988-989 annexin A7 GATCAGACTGTATTAAA 303 NM_032051 990-991 zinc finger protein 278 GATCTCCCACTGCGGGG 304 NM_032051 990-991 zinc finger protein 278 GATCGGCCCTACTAGAT 305 NM_032051 990-991 zinc finger protein 278 GATCAAAAAGCAAGCAG 306 NM_015972 992-993 polymerase (RNA) I polypeptide D, 16 kDa GATCACTTCAGCTGCCT 307 NM_019007 994-995 armadillo repeat containing, X-linked 6 GATCACCGACTGAAAAT 308 NM_002165 996-997 inhibitor of DNA binding 1, dominant negative helix-loop-helix protein GATCAATGAAGTGAGAA 309 NM_003094 998-999 small nuclear ribonucleoprotein polypeptide E GATCATCTCAGAAGTCT 310 NM_018683 1000-1001 zinc finger protein 313 GATCAGGAAGGACTTGT 311 NM_018683 1000-1001 zinc finger protein 313 GATCATTCCCATTTCAT 312 NM_002583 1002-1003 PRKC, apoptosis, WT1, regulator GATCGCTTTCTACACTG 313 NM_006926 1004-1005 surfactant, pulmonary- associated protein A2 GATCAGTTAGCTTTTAT 314 NM_014335 1006-1007 CREBBP/EP300 inhibitor 1 GATCAGTAGTTCAACAG 315 NM_175061 1008-1009 juxtaposed with another zinc finger gene 1 GATCCGATAAGTTATTG 316 NM_004707 1010-1011 APG12 autophagy 12- like (S. cerevisiae) GATCAGTGGGCACAGTT 317 NM_006818 1012-1013 ALL1-fused gene from chromosome 1q GATCAGTGCCAGAAGTC 318 NM_016303 1014-1015 WW domain binding protein 5 GATCAGAGAAGTAAGTT 319 NM_004871 1016-1017 golgi SNAP receptor complex member 1 GATCTCACTTTCCCCTT 320 NM_015373 1018-1019 PKD2 interactor, golgi and endoplasmic reticulum associated 1 GATCAGGCAGTTCCTGG 321 NM_213720 1020-1021 chromosome 22 open reading frame 16 GATCCTTGCCACAAGAA 322 NM_001156 1022-1023 annexin A7 GATCAAGAAAAATAAGG 323 NM_000999 1024-1025 ribosomal protein L38 GATCGATTTCTTTCCTC 324 NM_021102 1026-1027 serine protease inhibitor, Kunitz type, 2 GATCATAGAAGGCTATT 325 NM_181826 1028-1029 neurofibromin 2 (bilateral acoustic neuroma) GATCCGGTGCGCCATGT 326 NM_002638 1030-1031 protease inhibitor 3, skin-derived (SKALP) GATCGCAGTTTGGAAAC 327 NM_005461 1032-1033 v-maf musculoaponeurotic fibrosarcoma oncogene homolog B (avian) GATCAATTTCAAACCCT 328 NM_005461 1032-1033 v-maf musculoaponeurotic fibrosarcoma oncogene homolog B (avian) GATCTCCTATGAGAACA 329 NM_080392 1034-1035 protein tyrosine phosphatase type IVA, member 2 GATCAATACAATAACAA 330 NM_080392 1034-1035 protein tyrosine phosphatase type IVA, member 2 GATCCTACCACCTACTG 331 NM_018281 1036-1037 hypothetical protein FLJ10948 GATCATTTGTTTATTAA 332 NM_004763 1038-1039 integrin beta 1 binding protein 1 GATCAAAATGCTAAAAC 333 NM_178191 1040-1041 ATPase inhibitory factor 1 GATCTGGGGTGGGAGTA 334 NM_002773 1042-1043 protease, serine, 8 (prostasin) GATCATGCTTGTGTGAG 335 NM_018648 1044-1045 nucleolar protein family A, member 3 (H/ACA small nucleolar RNPs) GATCAAATATGTAAAAT 336 NM_138633 1046-1047 A kinase (PRKA) anchor protein 7 GATCAGACTTCTCAGCT 337 NM_006856 1048-1049 activating transcription factor 7 GATCATAGAAGGCTATT 338 NM_181827 1050-1051 neurofibromin 2 (bilateral acoustic neuroma) GATCCACCTTGGCCTCC 339 NM_003842 1052-1053 tumor necrosis factor receptor superfamily, member 10b GATCTCTGGCCCCTCAG 340 NM_198527 1054-1055 Similar to RIKEN cDNA 1110033O09 gene GATCCTCATTGAGCCAC 341 NM_024866 1056-1057 adrenomedullin 2 GATCCAGTGGGGTCCGG 342 NM_002475 1058-1059 myosin light chain 1 slow a GATCATTTTGTATTAAT 343 NM_017544 1060-1061 NF-kappa B repressing factor GATCAGAAAAAGAAAGA 344 NM_000982 1062-1063 ribosomal protein L21 GATCCTGTTCCTGTCAC 345 NM_203413 1064-1065 S-phase 2 protein GATCATGGTTCTCTTTG 346 NM_000202 1066-1067 iduronate 2-sulfatase (Hunter syndrome) GATCCTCTGACCGCTGG 347 NM_022365 1068-1069 DnaJ (Hsp40) homolog, subfamily C, member 1 GATCTGCTATTGCCAGC 348 NM_016399 1070-1071 hypothetical protein HSPC132 GATCCTGGAAATTGCAG 349 NM_001233 1072-1073 caveolin 2 GATCAGTCTCAAGTGTC 350 NM_003702 1074-1075 regulator of G-protein signalling 20 GATCAGGTTAGCAAATG 351 NM_004331 1076-1077 BCL2/adenovirus E1B 19 kDa interacting protein 3-like GATCAGTATGCTGTTTT 352 NM_004968 1078-1079 islet cell autoantigen 1, 69 kDa GATCTGGTTTCTAGCAA 353 NM_024096 1080-1081 XTP3-transactivated protein A GATCTAATTAAATAAAT 354 NM_000903 1082-1083 NAD(P)H dehydrogenase, quinone 1 GATCCTGGGTTTTTGTG 355 NM_017830 1084-1085 OCIA domain containing 1 GATCACCGACTGAAAAT 356 NM_181353 1086-1087 inhibitor of DNA binding 1, dominant negative helix-loop-helix protein GATCAGGTAACCAGAGC 357 NM_002488 1088-1089 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 2, 8 kDa GATCAGTGAACACTAAC 358 NM_016645 1090-1091 mesenchymal stem cell protein DSC92 GATCTCAGATGCTAGAA 359 NM_016567 1092-1093 BRCA2 and CDKN1A. interacting protein GATCGCTCTGCCCATGT 360 NM_016567 1092-1093 BRCA2 and CDKN1A interacting protein GATCAGCTCCGTGGGGC 361 NM_152398 1094-1095 OCIA domain containing 2 GATCATTGCCCAAAGTT 362 NM_152398 1094-1095 OCIA domain containing 2 GATCTGGCACTGTGGTT 363 NM_000998 1096-1097 ribosomal protein L37a GATCTGGCACTGTGGGT 364 NM_000998 1096-1097 ribosomal protein L37a GATCTCAGATGCTAGAA 365 NM_078468 1098-1099 BRCA2 and CDKN1A interacting protein GATCGCTCTGCCCATGT 366 NM_078468 1098-1099 BRCA2 and CDKN1A interacting protein GATCTGCTGTGGAATTG 367 NM_172316 1100-1101 Meis1, myeloid ecotropic viral integration site 1 homolog 2 (mouse) GATCGTTCTTGATTTTG 368 NM_032476 1102-1103 mitochondrial ribosomal protein S6 GATCTTGGTTTCATGTG 369 NM_032476 1102-1103 mitochondrial ribosomal protein S6 GATCATTCTTGATTTTG 370 NM_032476 1102-1103 mitochondrial ribosomal protein S6 GATCCATATGGAAAGAA 371 NM_014171 1104-1105 postsynaptic protein CRIPT GATCTGCCCCCACTGTC 372 NM_138929 1106-1107 diablo homolog (Drosophila) GATCGCCTAGTATGTTC 373 NM_052815 1108-1109 immediate early response 3 GATCAATGCTAATATGA 374 NM_005805 1110-1111 proteasome (prosome, macropain) 26S subunit, non-ATPase, 14 GATCAGCATCAGGCTGT 375 NM_012459 1112-1113 translocase of inner mitochondrial membrane 8 homolog B (yeast) GATCTGGAAGTGAAACA 376 NM_134265 1114-1115 WD repeat and SOCS box-containing 1 GATCCACGTGTGAGGGA 377 NM_182640 1116-1117 mitochondrial ribosomal protein S9 GATCACAGAAAAATTAA 378 NM_182640 1116-1117 mitochondrial ribosomal protein S9 GATCTCTCTGCGTTTGA 379 NM_012445 1118-1119 spondin 2, extracellular matrix protein GATCTCAGAAGTTTTGA 380 NM_138459 1120-1121 chromosome 6 open reading frame 68 GATCCGGACTTTTTAAA 381 NM_006339 1122-1123 high-mobility group 20B GATCATAGTTATTATAC 382 NM_032467 1124-1125 aspartate beta- hydroxylase GATCCTGCCCTGCTCTC 383 NM_003145 1126-1127 signal sequence receptor, beta (translocon- associated protein beta) GATCGATTGAGAAGTTA 384 NM_012110 1128-1129 cysteine-rich hydrophobic domain 2 GATCCAAGTACTCTCTC 385 NM_175081 1130-1131 purinergic receptor P2X, ligand-gated ion channel, 5 GATCATACACCTGCTCA 386 NM_001009 1132-1133 ribosomal protein S5 GATCCTGGATGCCACGA 387 NM_174889 1134-1135 hypothetical protein LOC91942 GATCCCTGCCACAAGTT 388 NM_006923 1136-1137 stromal cell-derived factor 2 GATCAGACGAGGCCATG 389 NM_006107 1138-1139 cisplatin resistance- associated overexpressed protein GATCTTTCAGGAAAGAC 390 NM_033011 1140-1141 plasminogen activator, tissue GATCTTTTAAAAATATA 391 NM_001914 1142-1143 cytochrome b-5 GATCGTTTTGTTTTGTT 392 NM_021149 1144-1145 coactosin-like 1 (Dictyostelium) GATCTATGGCCTCTGGT 393 NM_021643 1146-1147 tribbles homolog 2 (Drosophila) GATCCTAAATCATTTTG 394 NM_022783 1148-1149 DEP domain containing 6 GATCTAAGAAGAAACTA 395 NM_005765 1150-1151 ATPase, H+ transporting, lysosomal accessory protein 2 GATCTTGGTGTTCAAAA 396 NM_001497 1152-1153 UDP-Gal:betaGlcNAc beta 1,4- galactosyltransferase, polypeptide 1 GATCCCTCATCCCTGAA 397 NM_006793 1154-1155 peroxiredoxin 3 GATCTGCAGTGCTTCAC 398 NM_178181 1156-1157 CUB domain-containing protein 1 GATCTATGCCCTTGTTA 399 NM_033167 1158-1159 UDP-Gal:betaGlcNAc beta 1,3- galactosyltransferase, polypeptide 3 GATCTATGCCCTTGTTA 400 NM_033169 1160-1161 UDP-Gal:betaGlcNAc beta 1,3- galactosyltransferase, polypeptide 3 GATCAGTTTATTATTGA 401 NM_033169 1160-1161 UDP-Gal:betaGlcNAc beta 1,3- galactosyltransferase, polypeptide 3 GATCTATGCCCTTGTTA 402 NM_033168 1162-1163 UDP-Gal:betaGlcNAc beta 1,3- galactosyltransferase, polypeptide 3 GATCAGTTTATTATTGA 403 NM_033167 1158-1159 UDP-Gal:betaGlcNAc beta 1,3- galactosyltransferase, polypeptide 3 GATCTATGCCCTTGTTA 404 NM_003781 1164-1165 UDP-Gal:betaGlcNAc beta 1,3- galactosyltransferase, polypeptide 3 GATCAGTTTATTATTGA 405 NM_003781 1164-1165 UDP-Gal:betaGlcNAc beta 1,3- galactosyltransferase, polypeptide 3 GATCAGTTTATTATTGA 406 NM_033168 1162-1163 UDP-Gal:betaGlcNAc beta 1,3- galactosyltransferase, polypeptide 3 GATCGAGTCAAGATGAG 407 NM_013442 1166-1167 stomatin (EPB72)-like 2 GATCACCATGATGCAGA 408 NM_031905 1168-1169 SVH protein GATCCCGTGTGTGTGTG 409 NM_031905 1168-1169 SVH protein GATCATGGITCTGITTG 410 NM_006123 1170-1171 iduronate 2-sulfatase (Hunter syndrome) GATCCGCAGGCAGAAGC 411 NM_002775 1172-1173 Protease, serine, 11 (IGF binding) GATCGATGGTGGCGCTT 412 NM_138991 1174-1175 beta-site APP-cleaving enzyme 2 GATCTGCATCAGTTGTA 413 NM_001814 1176-1177 cathepsin C GATCTCTACTACCACAA 414 NM_001908 1178-1179 cathepsin B GATCTCTACTACCACAA 415 NM_147780 1180-1181 cathepsin B GATCTCTACTACCACAA 416 NM_147781 1182-1183 cathepsin B GATCTCTACTACCACAA 417 NM_147782 1184-1185 cathepsin B GATCTCTACTACCACAA 418 NM_147783 1186-1187 cathepsin B GATCGATGGTGGCGCTT 419 NM_012105 1188-1189 beta-site APP-cleaving enzyme 2 GATCTTTCAGGAAAGAC 420 NM_000931 1190-1191 plasminogen activator, tissue GATCAAATTGCAAAATA 421 NM_153705 1192-1193 KDEL (Lys-Asp-Glu- Leu) containing 2 GATCTTATTTTCTGAGA 422 NM_014584 1194-1195 ERO1-like (S. cerevisiae) GATCCACAAGGCCTGAG 423 NM_001185 1196-1197 alpha-2-glycoprotein 1, zinc GATCTAGGCCTCATCTT 424 NM_016352 1198-1199 carboxypeptidase A4 GATCCCTTTGAAATTTT 425 NM_001219 1200-1201 calumenin GATCTACAACATATAAA 426 NM_020648 1202-1203 twisted gastrulation homolog 1 (Drosophila) GATCAGTTTTTTCACCT 427 NM_001901 1204-1205 connective tissue growth factor GATCACAGTGTCAGAGA 428 NM_007224 1206-1207 neurexophilin 4 GATCGTTACTATGTGTC 429 NM_004541 1208-1209 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 1, 7.5 kDa GATCATTGACCTCTGTG 430 NM_006459 1210-1211 SPFH domain family, member 1 GATCTGAAGCCCAGGTT 431 NM_024514 1212-1213 cytochrome P450, family 2, subfamily R, polypeptide 1 GATCTGTTAAAAAAAAA 432 NM_147159 1214-1215 opioid receptor, sigma 1 GATCTTTCAGGAAAGAC 433 NM_000930 1216-1217 plasminogen activator, tissue GATCATAAGACAATGGA 434 NM_001657 1218-1219 amphiregulin (schwannoma-derived growth factor) GATCAGTCTTTATTAAT 435 NM_013995 1220-1221 lysosomal-associated membrane protein 2 GATCCAGGCTCACTGTG 436 NM_005250 1222-1223 forkhead box L1 GATCAAATAATGCGACG 437 NM_018064 1224-1225 chromosome 6 open reading frame 166 GATCTTGGTTTTCCATG 438 NM_003000 1226-1227 succinate dehydrogenase complex, subunit B, iron sulfur (Ip) GATCTGTTAGTCAAGTG 439 NM_005313 1228-1229 glucose regulated protein, 58 kDa GATCATTTCTGGTAAAT 440 NM_005313 1228-1229 glucose regulated protein, 58 kDa GATCAAAGCACTCTTCC 441 NM_005313 1228-1229 glucose regulated protein, 58 kDa GATCATGCCAAGTGGTG 442 NM_021233 1230-1231 deoxyribonuclease II beta GATCATCGCCTCCCTGG 443 NM_006216 1232-1233 serine (or cysteine) proteinase inhibitor, clade E (nexin, plasminogen activator inhibitor type 1), member 2 GATCACCAGGCTGCCCA 444 NM_016504 1234-1235 mitochondrial ribosomal protein L27 GATCGGATGGGCAAGTC 445 NM_002178 1236-1237 insulin-like growth factor binding protein 6 GATCTCAAGACCAAAGA 446 NM_030810 1238-1239 thioredoxin domain containing 5 GATCTCACATTGTGCCC 447 NM_014254 1240-1241 transmembrane protein 5 GATCAGTCTTTATTAAT 448 NM_002294 1242-1243 lysosomal-associated membrane protein 2 GATCAGAGAAGATGATA 449 NM_000640 1244-1245 interleukin 13 receptor, alpha 2 GATCAGGTAACCAGAGC 450 NM_000591 1246-1247 CD14 antigen GATCATCAGTAAATTTG 451 NM_031284 1248-1249 ADP-dependent glucokinase GATCAATAAAATGTGAT 452 NM_002658 1250-1251 plasminogen activator, urokinase GATCCCTCGGGTTTTGT 453 NM_006350 1252-1253 follistatin GATCTTGCAACTCCATT 454 NM_006350 1252-1253 follistatin GATCCAGCATGGAGGCC 455 NM_018664 1254-1255 Jun dimerization protein p21SNFT GATCATTGTGAAGGCAG 456 NM_001511 1256-1257 chemokine (C-X-C motif) ligand 1 (melanoma growth stimulating activity, alpha) GATCTGCCAGCAGTGTT 457 NM_002004 1258-1259 farnesyl diphosphate synthase (farnesyl pyrophosphate synthetase, dimethylallyltranstransferase, geranyltranstransferase) GATCAGAGGTTACTAGG 458 NM_006408 1260-1261 anterior gradient 2 homolog (Xenopus laevis) GATCCACAGGGGTGGTG 459 NM_000602 1262-1263 serine (or cysteine) proteinase inhibitor, clade E (nexin, plasminogen activator inhibitor type 1), member 1 GATCACAAGGGGGGGAT 460 NM_016588 1264-1265 neuritin 1 GATCTCTGTTTTGACTA 461 NM_004109 1266-1267 ferredoxin 1 GATCTAACCTGGCTTGT 462 NM_004109 1266-1267 ferredoxin 1 GATCAGCAAGTGTCCTT 463 NM_000935 1268-1269 procollagen-lysine, 2-oxoglutarate 5-dioxygenase 2 GATCTAGTGGTTCACAC 464 NM_003236 1270-1271 transforming growth factor, alpha GATCAAACAGTTTCTGG 465 NM_016139 1272-1273 coiled-coil-helix-coiled- coil-helix domain containing 2 GATCATCAAGAAAAAAG 466 NM_018464 1274-1275 chromosome 10 open reading frame 70 GATCCCAGAGAGCAGCT 467 NM_002421 1276-1277 matrix metalloproteinase 1 (interstitial collagenase) GATCTTGTGTATTTTTG 468 NM_020440 1278-1279 prostaglandin F2 receptor negative regulator GATCTATGTTCTCTCAG 469 NM_013363 1280-1281 procollagen C- endopeptidase enhancer 2 GATCAGCAAGTGTCCTT 470 NM_182943 1282-1283 procollagen-lysine, 2- oxoglutarate 5- dioxygenase 2 GATCATGTGCTACTGGT 471 NM_003172 1284-1285 surfeit 1 GATCTGTAAATAAAATC 472 NM_130781 1286-1287 RAB24, member RAS oncogene family GATCAGGGCTGAGGGTA 473 NM_000157 1288-1289 glucosidase, beta; acid (includes glucosylceramidase) GATCCTCCTATGTTGTT 474 NM_005551 1290-1291 kallikrein 2, prostatic GATCAGAGATGCACCAC 475 NM_002997 1292-1293 syndecan 1 GATCTGTCTGTTGCTTG 476 NM_005570 1294-1295 lectin, mannose-binding, 1 GATCACCATGAAAGAAG 477 NM_003873 1296-1297 neuropilin 1 GATCTGTTAAAAAAAAA 478 NM_005866 1298-1299 opioid receptor, sigma 1 GATCAATTCCCTTGAAT 479 NM_138322 1300-1301 proprotein convertase subtilisin/kexin type 6 GATCCCAGACCAACCCT 480 NM_024642 1302-1303 UDP-N-acetyl-alpha-D- galactosamine:polypeptide N-acetylgalactosaminyltransferase 12 (GalNAc-T12) GATCATCACAGTTTGAG 481 NM_002425 1304-1305 matrix metalloproteinase 10 (stromelysin 2) GATCGGAACAGCTCCTT 482 NM_178154 1306-1307 fucosyltransferase 8 (alpha (1,6) fucosyltransferase) GATCGGAACAGCTCCTT 483 NM_178155 1308-1309 fucosyltransferase 8 (alpha (1,6) fucosyltransferase) GATCGGAACAGCTCCTT 484 NM_178156 1310-1311 fucosyltransferase 8 (alpha (1,6) fucosyltransferase) GATCTGTGGGCCCAGTC 485 NM_004077 1312-1313 citrate synthase GATCAACCTTAAAGGAA 486 NM_000143 1314-1315 fumarate hydratase GATCTTCTACTTGCCTG 487 NM_000302 1316-1317 procollagen-lysine 1, 2-oxoglutarate 5-dioxygenase 1 GATCACCAGCCATGTGC 488 NM_004390 1318-1319 cathepsin H GATCACCGGAGGTCAGT 489 NM_016026 1320-1321 retinol dehydrogenase 11 (all-trans and 9-cis) GATCTATTTTATGCATG 490 NM_020792 1322-1323 KIAA1363 protein GATCTGTTAAAAAAAAA 491 NM_147157 1324-1325 opioid receptor, sigma 1 GATCATTTTGGTTCGTG 492 NM_016417 1326-1327 chromosome 14 open reading frame 87 GATCACTTGTGTACGAA 493 NM_024641 1328-1329 mannosidase, endo-alpha GATCCCTCCACCCCCAT 494 NM_001441 1330-1331 fatty acid amide hydrolase GATCCAAAGTCATGTGT 495 NM_058172 1332-1333 anthrax toxin receptor 2 GATCCATAAATATTTAT 496 NM_058172 1332-1333 anthrax toxin receptor 2 GATCTGCCTGCATCCTG 497 NM_003225 1334-1335 trefoil factor 1 (breast cancer, estrogen- inducible sequence expressed in) GATCCAGTGTCCATGGA 498 NM_007085 1336-1337 follistatin-like 1 GATCAATTCCCTTGAAT 499 NM_138324 1338-1339 proprotein convertase subtilisin/kexin type 6 GATCCGTGTGCTTGGGC 500 NM_018143 1340-1341 kelch-like 11 (Drosophila) GATCCAGGGTCCCCCAG 501 NM_004911 1342-1343 protein disulfide isomerase related protein (calcium-binding protein, intestinal-related) GATCATGGGACCCTCTC 502 NM_003032 1344-1345 sialyltransferase 1 (beta- galactoside alpha-2,6- sialyltransferase) GATCATGGGACCCTCTC 503 NM_173216 1346-1347 sialyltransferase 1 (beta- galactoside alpha-2,6- sialyltransferase) GATCTCACTGTTATTAT 504 NM_007115 1348-1349 tumor necrosis factor, alpha-induced protein 6 GATCCTGTATCCAAATC 505 NM_007115 1348-1349 tumor necrosis factor, alpha-induced protein 6 GATCAGTTTTCTCTTAA 506 NM_024769 1350-1351 adipocyte-specific adhesion molecule GATCTACCAGATAACCT 507 NM_000522 1352-1353 homeo box A13 GATCCTAGTAATTGCCT 508 NM_054034 1354-1355 fibronectin 1 GATCAATGCAACGACGT 509 NM_006833 1356-1357 COP9 constitutive photomorphogenic homolog subunit 6 (Arabidopsis) GATCAATTCCCTTGAAT 510 NM_138325 1358-1359 proprotein convertase subtilisin/kexin type 6 GATCAATTCCCTTGAAT 511 NM_138323 1360-1361 proprotein convertase subtilisin/kexin type 6 GATCCCAGAGGGATGCA 512 NM_024040 1362-1363 CUE domain containing 2 GATCATCAAAAATGCTA 513 NM_017898 1364-1365 hypothetical protein FLJ20605 GATCCCTCGGGTTTTGT 514 NM_013409 1366-1367 follistatin GATCTTGCAACTCCATT 515 NM_013409 1366-1367 follistatin GATCTTGTTAATGCATT 516 NM_001873 1368-1369 carboxypeptidase E GATCAAAGGTTTAAAGT 517 NM_001627 1370-1371 activated leukocyte cell adhesion molecule GATCACCAAGATGCTTC 518 NM_018371 1372-1373 chondroitin beta1,4 N- acetylgalactosaminyltransferase GATCAAATGTGCCTTAA 519 NM_014918 1374-1375 carbohydrate (chondroitin) synthase 1 GATCTTCGGCCTCATTC 520 NM_017860 1376-1377 hypothetical protein FLJ20519 GATCCCTTCTGCCCTGG 521 NM_022367 1378-1379 sema domain, immunoglobulin domain (Ig), transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 4A GATCCAACCGACTGAAT 522 NM_006670 1380-1381 trophoblast glycoprotein GATCTCTGCAGATGCCA 523 NM_004750 1382-1383 cytokine receptor-like factor 1 GATCACAAAATGTTGCC 524 NM_001077 1384-13852 UDP glycosyltransferase family, polypeptide B17 GATCTCTCTTTCTCTCT 525 NM_031882 1386-1387 protocadherin alpha subfamily C, 1 GATCTCTCTTTCTCTCT 526 NM_031860 1388-1389 protocadherin alpha 10 GATCTCTCTTTCTCTCT 527 NM_018906 1390-1391 protocadherin alpha 3 GATCTCTCTTTCTCTCT 528 NM_031411 1392-1393 protocadherin alpha 1 GATCACAGGCGTGAGCT 529 NM_032620 1394-1395 GTP binding protein 3 (mitochondrial) GATCAACATCTTTTCTT 530 NM_004343 1396-1397 calreticulin GATCTCTGATTTAACCG 531 NM_002185 1398-1399 interleukin 7 receptor GATCTCTCTTTCTCTCT 532 NM_031497 1400-1401 protocadherin alpha 3 GATCCATTTTTAATGGT 533 NM_198278 1402-1403 hypothetical protein LOC255743 GATCTTTTCTAAATGTT 534 NM_005699 1404-1405 interleukin 18 binding protein GATCTCTCTTTCTCTCT 535 NM_031410 1406-1407 protocadherin alpha 1 GATCGGTGCGTTCTCCT 536 NM_005561 1408-1409 lysosomal-associated membrane protein 1 GATCTTTTCTAAATGTT 537 NM_173042 1410-1411 interleukin 18 binding protein GATCTTTTCTAAATGTT 538 NM_173043 1412-1413 interleukin 18 binding protein GATCTCTCTTTCTCTCT 539 NM_031496 1414-1415 protocadherin alpha 2 GATCCTGTTGGATGTGA 540 NM_080927 1416-1417 discoidin, CUB and LCCL domain containing 2 GATCTCTCTTTCTCTCT 541 NM_031864 1418-1419 protocadherin alpha 12 GATCTCTCTTTCTCTCT 542 NM_031849 1420-1421 protocadherin alpha 6 GATCCTGTGCTTCTGCA 543 NM_006464 1422-1423 trans-golgi network protein 2 GATCTCTCTTTCTCTCT 544 NM_031865 1424-1425 protocadherin alpha 13 GATCTGATGAAGTATAT 545 NM_022746 1426-1427 hypothetical protein GATCACTTGTCTTGTGG 546 NM_006988 1428-1429 FLJ22390 a disintegrin-like and metalloprotease (reprolysin type) with thrombospondin type 1 motif, 1 GATCTTTTCTAAATGTT 547 NM_173044 1430-1431 interleukin 18 binding protein GATCTCTCTTTCTCTCT 548 NM_031856 1432-1433 protocadherin alpha 8 GATCTCTCTTTCTCTCT 549 NM_031500 1434-1435 protocadherin alpha 4 GATCAGCACTGCCAGTG 550 NM_016592 1436-1437 GNAS complex locus GATCCGGAAAGATGAAT 551 NM_144640 1438-1439 interleukin 17 receptor E GATCTCTCTTTCTCTCT 552 NM_031501 1440-1441 protocadherin alpha 5 GATCTCTCTTTCTCTCT 553 NM_031495 1442-1443 protocadherin alpha 2 GATCTAATGTAAAATCC 554 NM_002354 1444-1445 tumor-associated calcium signal transducer 1 GATCTTCTTTTGTAATG 555 NM_032780 1446-1447 transmembrane protein 25 GATCAATAATAATGAGG 556 NM_001001390 1448-1449 CD44 antigen (homing function and Indian blood group system) GATCAACAGTGGCAATG 557 NM_001001390 1448-1449 CD44 antigen (homing function and Indian blood group system) GATCAACAGTGGCAATG 558 NM_001001391 1450-1451 CD44 antigen (homing function and Indian blood group system) GATCAATAATAATGAGG 559 NM_001001391 1450-1451 CD44 antigen (homing function and Indian blood group system) GATCATTGCTCCTTCTC 560 NM_004872 1452-1453 chromosome 1 open reading frame 8 GATCTCTGCATTTTATA 561 NM_020198 1454-1455 GK001 protein GATCTATGAAATCTGTG 562 NM_020198 1454-1455 GK001 protein GATCTCTCTTTCTCTCT 563 NM_018901 1456-1457 protocadherin alpha 10 GATCACTGGAGCTGTGG 564 NM_002116 1458-1459 major histocompatibility complex, class I, A GATCATCCAGTTTGCTT 565 NM_004540 1460-1461 neural cell adhesion molecule 2 GATCAAAATTGTTACCC 566 NM_004540 1460-1461 neural cell adhesion molecule 2 GATCAACAGTGGCAATG 567 NM_001001389 1462-1463 CD44 antigen (homing function and Indian blood group system) GATCAATAATAATGAGG 568 NM_001001389 1462-1463 CD44 antigen (homing function and Indian blood group system) GATCAACAGTGGCAATG 569 NM_000610 1464-1465 CD44 antigen (homing function and Indian blood group system) GATCAATAATAATGAGG 570 NM_000610 1464-1465 CD44 antigen (homing function and Indian blood group system) GATCCATACTGTTTGGA 571 NM_001792 1466-1467 cadherin 2, type 1, N- cadherin (neuronal) GATCTGCATTTTCAGAA 572 NM_015544 1468-1469 DKFZP564K1964 protein GATCCCATTTTTTGGTA 573 NM_000574 1470-1471 decay accelerating factor for complement (CD55, Cromer blood group system) GATCTGCAGTGCTTCAC 574 NM_022842 1472-1473 CUB domain-containing protein 1 GATCTGTTAAAAAAAAA 575 NM_147160 1474-1475 opioid receptor, sigma 1 GATCATAGGTCTGGACA 576 NM_014045 1476-1477 low density lipoprotein receptor-related protein 10 GATCTAATACTACTGTC 577 NM_001110 1478-1479 a disintegrin and metalloproteinase domain 10 GATCTCTTGAGGCTGGG 578 NM_016371 1480-1481 hydroxysteroid (17-beta) dehydrogenase 7 GATCGTTCATTGCCTTT 579 NM_001746 1482-1483 calnexin GATCTCTCTTTCTCTCT 580 NM_018900 1484-1485 protocadherin alpha 1 GATCTGACCTGGTGAGA 581 NM_004393 1486-1487 dystroglycan 1 (dystrophin-associated glycoprotein 1) GATCATCTTTCCTGTTC 582 NM_002117 1488-1489 major histocompatibility complex, class I, C GATCGTAAAATTTTAAG 583 NM_003816 1490-1491 a disintegrin and metalloproteinase domain 9 (meltrin gamma) GATCTCTCTTTCTCTCT 584 NM_018904 1492-1493 protocadherin alpha 13 GATCTCTCTTTCTCTCT 585 NM_018911 1494-1495 protocadherin alpha 8 GATCTCTCTTTCTCTCT 586 NM_018905 1496-1497 protocadherin alpha 2 GATCTCTCTTTCTCTCT 587 NM_018903 1498-1499 protocadherin alpha 12 GATCTCTCTTTCTCTCT 588 NM_018907 1500-1501 protocadherin alpha 4 GATCTCTCTTTCTCTCT 589 NM_018908 1502-1503 protocadherin alpha 5 GATCCGGAAAGATGAAT 590 NM_153480 1504-1505 interleukin 17 receptor E GATCCGGAAAGATGAAT 591 NM_153483 1506-1507 interleukin 17 receptor E GATCTCTGTAATTTTAT 592 NM_021923 1508-1509 fibroblast growth factor receptor-like 1 GATCTAAGAGATTAATA 593 NM_004362 1510-1511 calmegin

Example 2 Identification of Secreted Proteins by Computational Analysis of MPSS Signature Sequences

Secreted proteins can readily be exploited for blood cancer diagnosis and prognosis. As such, the differentially expressed genes identified in Example 1 were further analyzed to determine how many of the differentially expressed genes encode secreted proteins. Proteins with signal peptides (classical secretory proteins) were predicted using the same criteria described by Chen et al., Mamm Genome, 14: 859-865, 2003, with the SignalP 3.0 server developed by The Center for Biological Sequence Analysis, Lyngby, Denmark (http colon double slash www dot cbs dot dtu dot dk slash services slash SignalP-3.0; see also, J. D. Bendtsen, et al., J. Mol. Biol., 340:783-795, 2004.) and the TMHMM2.0 server (see for example A. Krogh, et al., Journal of Molecular Biology, 305(3):567-580, January 2001; E. L. L. Sonnhammer, et al., In J. Glasgow, T. Littlejohn, F. Major, R. Lathrop, D. Sankoff, and C. Sensen, editors, Proceedings of the Sixth International Conference on Intelligent Systems for Molecular Biology, pages 175-182, Menlo Park, Calif., 1998. AAAI Press). Putatively nonclassical secretory secreted proteins (without signal peptides) were predicted based on the SecretomeP 1.0 server, (httpcolon double slash www dot cbs dot dtu dot dkslash services slash SecretomeP-1.0 slash) and required an odds ratio score >3.

Five hundred and twenty one signatures belonging to 460 genes potentially encoding secreted proteins (Table 3) were identified. Among these, 287 (259 genes) and 234 (201 genes) signatures were overexpressed or underexpressed in CL1 cells compared with LNCaP cells. Thus these proteins can be used in blood diagnostics to follow prostate cancer progression.

Example 3 Prostate Cancer Diagnostics Using Multiparameter Analysis

This example describes a multiparameter diagnostic fingerprint using the WDR19 prostate-specific secreted protein in combination with PSA. The WDR19 prostate-specific protein is diagnostically superior to PSA when used alone and further improved prostate cancer detection when used in combination with PSA.

WDR19 was previously identified as relatively tissue-specific by cDNA array studies and Northern blot analysis (see e.g., U.S. Patent Application Publication No. 20020150893). This protein was selected, expressed as protein, purified and antibodies were made against it, all using standard techniques known in the art (the cDNA encoding the WDR19 protein is provided in SEQ ID NO:1, the amino acid sequence is provided in SEQ ID NO:2). The WDR19-specific antibody was shown to be an excellent tissue-specific marker of prostate cancer with staining of the specific epithelial cells being directly proportional to the progression of the cancer. In this regard it is very different from the well-established PSA marker which is not a good prostate tissue cancer marker.

The WDR19 antibodies and those for the well-established PSA prostate cancer blood marker were used to analyze 10 blood samples from normal individuals, 10 blood samples from early prostate cancer patients and 10 blood samples from late prostate cancer patients. The results showed that WDR19 reacted against no normals, against 5/10 early cancers, and against 5/10 late cancers, whereas PSA reacted against no normals, no early cancers and 7/10 late cancers. The two markers together detected all the late cancers. Thus the mutiparameter analysis of blood markers (e.g. the analyses of multiple markers) for prostate cancer was far more powerful than using each marker alone.

Accordingly, the results show a molecular blood fingerprint that comprises the WDR19 and PSA proteins. This fingerprint allows superior diagnostic power to PSA alone and further improves prostate cancer detection.

WDR19 was also shown to be an effective histochemical marker for prostate cancer. Two hundred and seventy-five tissue cores that contain both stromal and epithelial cells from cancer patients, 17 from benign prostatic hyperplasia (BPH) and 12 from normal individuals were examined. The mean WDR19 protein staining intensities were 2.52 [standard error (S.E.), 0.05; 95% confidence interval (CI), 2.41-2.61] for prostate cancer; 1.03 BPH (S.E. 0.03; 95% CI, 0.96-1.09); and 1.0 (S.E., 0, 95% CI 1.0-1.0) for normal individuals. Pair-wise comparisons (using independent t-test) demonstrated that WDR19 staining intensity is significantly different between prostate cancer and BPH (mean difference 1.49; P<0.0001) and between prostate cancers and normal (mean difference 1.52; P<0.0001). These data suggested that WDR19, in addition to being a prostate-specific blood biomarker, is a quantitative cancer-specific marker for prostate tissues.

Example 4 Identification of Organ-Specific Secreted Proteins Using MPSS and Computational Analysis

MPSS as described in Example 1 and in the detailed description, was used to identify more than 2 million transcripts from each of the prostate cell lines (see Example 1) and in normal prostate tissue. The MPSS signature sequences from normal prostate were compared against 29 other tissues each with about 1 million or more mRNA transcripts. This comparison revealed that about 300 of these transcripts are organ-specific and about 60 of these organ-specific transcripts are potentially secreted into the blood. (See Table 4).

TABLE 4 PROSTATE-SPECIFIC PROTEINS POTENTIALLY SECRETED INTO BLOOD SEQ Accession ID No. NO: Annotations/Description NP_001176 1512 alpha-2-glycoprotein 1, zinc; Alpha-2-glycoprotein, zinc [Homo sapiens] NP_001719 1513 basigin isoform 1; OK blood group; collagenase stimulatory factor; M6 antigen; extracellular matrix metalloproteinase inducer [Homo sapiens] NP_940991 1514 basigin isoform 2; OK blood group; collagenase stimulatory factor; M6 antigen; extracellular matrix metalloproteinase inducer [Homo sapiens] NP_004039 1515 beta-2-microglobulin precursor [Homo sapiens] NP_002434 1516 beta-microseminoprotein isoform a precursor; seminal plasma beta-inhibin; prostate secreted seminal plasma protein; immunoglobulin binding factor; prostatic secretory protein 94 [Homo sapiens] NP_619540 1517 beta-microseminoprotein isoform b precursor; seminal plasma beta-inhibin; prostate secreted seminal plasma protein; immunoglobulin binding factor; prostatic secretory protein 94 [Homo sapiens] NP_817089 1518 cadherin-like 26 isoform a; cadherin-like protein VR20 [Homo sapiens] NP_068582 1519 cadherin-like 26 isoform b; cadherin-like protein VR20 [Homo sapiens] NP_001864 1520 carboxypeptidase E precursor [Homo sapiens] NP_004807 1521 chromosome 9 open reading frame 61; Friedreich ataxia region gene X123 [Homo sapiens] NP_001271 1522 cold inducible RNA binding protein; Cold-inducible RNA-binding protein; cold inducible RNA-binding protein; glycine-rich RNA binding protein [Homo sapiens] NP_008977 1523 elastin microfibril interfacer 1; TNF? elastin microfibril interface located protein; elastin microfibril interface located protein [Homo sapiens] NP_004104 1524 fibroblast growth factor 12 isoform 2; fibroblast growth factor 12B; fibroblast growth factor homologous factor 1; myocyte-activating factor; fibroblast growth factor FGF-12b [Homo sapiens] NP_005962 1525 FXYD domain containing ion transport regulator 3 isoform 1 precursor; phospholemman-like protein; FXYD domain-containing ion transport regulator 3 [Homo sapiens] NP_068710 1526 FXYD domain containing ion transport regulator 3 isoform 2 precursor; phospholemman-like protein; FXYD domain-containing ion transport regulator 3 [Homo sapiens] NP_006352 1527 homeo box B13; homeobox protein HOX-B13 [Homo sapiens] NP_002139 1528 homeo box D10; homeobox protein Hox-D10; homeo box 4D; Hox-4 NP_000513 1529 homeobox protein A13; homeobox protein HOXA13; homeo box 1J; transcription factor HOXA13 [Homo sapiens] NP_060819 1530 hypothetical protein FLJ11175 [Homo sapiens] NP_078985 1531 hypothetical protein FLJ14146 [Homo sapiens] NP_061894 1532 hypothetical protein FLJ20010 [Homo sapiens] NP_115617 1533 hypothetical protein FLJ23544; QM gene; DNA segment on chromosome X (unique) 648 expressed sequence; 60S ribosomal protein L10; tumor suppressor QM; Wilms tumor-related protein; laminin receptor homolog [Homo sapiens] NP_057582 1534 hypothetical protein HSPC242 [Homo sapiens] NP_116285 1535 hypothetical protein MGC14388 [Homo sapiens] NP_116293 1536 hypothetical protein MGC14433 [Homo sapiens] NP_077020 1537 hypothetical protein MGC4309 [Homo sapiens] NP_061074 1538 hypothetical protein PRO1741 [Homo sapiens] NP_563614 1539 hypothetical protein similar to KIAA0187 gene product [Homo sapiens] NP_951038 1540 I-mfa domain-containing protein isoform p40 [Homo sapiens] NP_005542 1541 kallikrein 2, prostatic isoform 1; glandular kallikrein 2 [Homo sapiens] NP_004908 1542 kallikrein 4 preproprotein; protease, serine, 17; enamel matrix serine protease 1; kallikrein-like protein 1; protase; androgen-regulated message 1 [Homo sapiens] NP_002328 1543 low density lipoprotein receptor-related protein associated protein 1; lipoprotein receptor associated protein; alpha-2-MRAP; alpha-2- macroglobulin receptor-associated protein 1; low density lipoprotein-related protein-associated protein 1; low density li NP_859077 1544 low density lipoprotein receptor-related protein binding protein [Homo sapiens] NP_000897 1545 natriuretic peptide receptor A/guanylate cyclase A (atrionatriuretic peptide receptor A); Natriuretic peptide receptor A/guanylate cyclase A [Homo sapiens] NP_ 085048 1546 Nedd4 family interacting protein 1; Nedd4 WW domain-binding protein 5 [Homo sapiens] NP_000896 1547 neuropeptide Y [Homo sapiens] NP_039227 1548 olfactory receptor, family 10, subfamily H, member 2 [Homo sapiens] NP_000599 1549 orosomucoid 2; alpha-1-acid glycoprotein, type 2 [Homo sapiens] NP_002643 1550 prolactin-induced protein; prolactin-inducible protein [Homo sapiens] NP_057674 1551 prostate androgen-regulated transcript 1 protein; prostate-specific and androgen-regulated cDNA 14D7 protein [Homo sapiens] NP_001639 1552 prostate specific antigen isoform 1 preproprotein; gamma-seminoprotein; semenogelase; seminin; P-30 antigen [Homo sapiens] NP_665863 1553 prostate specific antigen isoform 2; gamma- seminoprotein; semenogelase; seminin; P-30 antigen [Homo sapiens] NP_001090 1554 prostatic acid phosphatase precursor [Homo sapiens] NP_001000 1555 ribosomal protein S5; 40S ribosomal protein S5 [Homo sapiens] NP_005658 1556 ring finger protein 103; Zinc finger protein expressed in cerebellum; zinc finger protein 103 homolog (mouse) [Homo sapiens] NP_937761 1557 ring finger protein 138 isoform 2 [Homo sapiens] NP_002998 1558 semenogelin I isoform a preproprotein [Homo sapiens] NP_937782 1559 semenogelin I isoform b preproprotein [Homo sapiens] XP_353669 1560 similar to HIC protein isoform p32 [Homo sapiens] NP_003855 1561 sin3 associated polypeptide p30 [Homo sapiens] NP_036581 1562 six transmembrane epithelial antigen of the prostate; six transmembrane epithelial antigen of the prostate (NOTE: non-standard symbol and name) [Homo sapiens] NP_008868 1563 SMT3 suppressor of mif two 3 homolog 2; SMT3 (suppressor of mif two 3, yeast) homolog 2 [Homo sapiens] NP_066568 1564 solute carrier family 15 (H+/peptide transporter), member 2 [Homo sapiens] NP_055394 1565 solute carrier family 39 (zinc transporter), member 2 [Homo sapiens] NP_003209 1566 telomeric repeat binding factor 1 isoform 2; Telomeric repeat binding factor 1; telomeric repeat binding protein 1 [Homo sapiens] NP_110437 1567 thioredoxin domain containing 5 isoform 1; thioredoxin related protein; endothelial protein disulphide isomerase [Homo sapiens] NP_004863 1568 thymic dendritic cell-derived factor 1; liver membrane-bound protein [Homo sapiens] NP_665694 1569 TNF receptor-associated factor 4 isoform 2; tumor necrosis receptor-associated factor 4A; malignant 62; cysteine-rich domain associated with ring and TRAF domain [Homo sapiens] NP_005647 1570 transmembrane protease, serine 2; epitheliasin [Homo sapiens] NP_008931 1571 uroplakin 1A [Homo sapiens] NP_036609 1572 WW domain binding protein 1 [Homo sapiens] NP_009062 1573 zinc finger protein 75 [Homo sapiens]

Example 5 Comparison of Localized Prostate Cancer and Prostate Cancer Metastases in the Liver

In an additional experiment, the transcriptome from normal prostate tissue was compared to the transcriptome of each of the LNCaP and CL-1 prostate cancer cell lines. The comparison showed that the transcriptomes were distinct for the normal tissue, the early prostate cancer and the late prostate cancer. An additional comparison was carried out between localized prostate cancer and metastases in the liver. About 6,000 genes were identified that were significantly changed between the localized prostate cancer and the metastasized cancer and again, many of the changed genes encoded secreted proteins that can be part of the blood fingerprints indicative of the more advanced disease status of metastases. The metastases-altered blood fingerprints may indicate the site of metastases.

These experiments demonstrate that there are continuous changes in the two types of networks as prostate cancer progresses—from localized to androgen independence to metastases. These graded network transitions suggest that one will be able to detect the very earliest stages of prostate cancer and, accordingly, that the organ-specific, molecular blood fingerprints approach described herein will also permit a very early diagnosis of prostate and other types of cancers.

Example 6 MPSS Analysis in a Yeast Model System

This experiment demonstrates perturbation-specific fingerprints of patterns of gene expression for nuclear, cytoplasmic, membrane-bound and secreted proteins in the yeast metabolic system that converts the sugar galactose into glucose-6-phosphate (the gal system).

The gal systems includes 9 proteins. In the course of studying how this systems works, 9 new strains of yeast were created, each with a different one of the 9 relevant genes destroyed (gene knockouts). Yeast is a single celled eukaryote organism with about 6,000 genes. The expression patterns of each of the 6,000 genes was studied in the wild type yeast and each of the 9 knockout strains. The data from these experiments showed: 1) the wild type and each of knock out strains exhibited statistically significant changes in patterns of gene expression from the wild type strain ranging from 89 to 465 altered patterns of gene expression; 2) each of these patterns of changed gene expression were unique; and 3) on average about 15% of the genes with changed expression patterns encoded proteins that were potentially secreted (as determined by computational analysis from the sequence of the gene). These genes are as follows: (listed by gene name as available through the public yeast genome database at http://www.yeastgenome.org/. The genomic DNA, cDNA and amino acid sequences corresponding to each of the listed genes are publicly available, for example, through the yeast genome database.) YGL102C, YGL069C, YLL044W, YMR321C, YKL153W, YMR195W, YHL015W, YNL096C, YGR030C, YDR123C, YKL186C, YOR234C, YKL001C, YJL188C, YDL023C, YPL143W, YEL039C, YKL006W, YGR280C, YBR285W, YKR091W, YDR064W, YBR047W, YGR243W, YOR309C, YDR461W, YHR053C, YHR055C, YGR148C, YGL187C, YIL018W, YFR003C, YPL107W, YBR185C, YNR014W, YJL067W, YDR451C, YGL031C, YHR141C, YNL162W, YBR046C, YNL036W, YDL136W, YDL191W, YLR257W, YNL057W, YGL068W, YKR057W, YLR201C, YHL001W, YDR010C, YPL138C, YOR312C, YPL276W, YML114C, YLR327C, YBR191W, YOR257W, YOR096W, YPL223C, YJL136C, YAL044C, YER079W, YMR107W, YPL079W, YDR175C, YGR035C, YDR153C, YDR337W, YOR167C, YMR194W, YOR194C, YHR090C, YGR110W, YMR242C, YHR198C, YPL177C, YLR164W, YMR143W, YDL083C, YLR325C, YOR203W, YMR193W, YLR062C, YOR383C, YLR300W, YJL079C, YJL158C, YHR139C, YGL032C, YER150W, YNL160W, YDR382W, YMR305C, YKL096W, YKR013W, YCL043C, YLR042C, YDR055W, YPL163C, YEL040W, YJL171C, YLR121C, YDR382W, YLR250W, YGR189C, YJL159W, YMR215W, YDR519W, YIL162W, YKL163W, YDR518W, YDR534C, YPR157W, YML130C, YML128C, YBR092C, YDR032C, YLR120C, YBR093C, YHR215W, YAR071W, YDL130W, YDR144C, YPR123C, YGR174C, YOR327C, YNL058C, YGR265W, YGR160W, YIL117C, YOL053W, YGR236C, YGR060W, YKL120W, YDL046W, YHR132C, YMR058W, YLR332W, YKR061W, YEL001C, YKL154W, YKL073W, YMR238W, YJR020W, YIL136W, YHL028W, YDL010W, YLR339C, YNL217W, YHR063C.

The different knockout strains can be thought of as analogous to genetic disease mutants. Accordingly, these data further support the notion that each disease has a unique expression fingerprint and that each disease generates unique collections of secreted proteins that constitute molecular fingerprints capable of identifying the corresponding disease.

Example 7 Identification of Prostate-Specific/Enriched Genes Using a 2.5 Fold Over-Expression Cut-Off

Organ specific/enriched expression can be determined by the ratio of the expression (e.g., measured in transcripts per million (tpm)) in a particular organ as compared to other organs. In this example, prostate enriched/specific expression was analyzed by comparing the expression level (tpm counts) of MPSS signature sequences identified from normal prostate tissue to their corresponding expression levels in 33 normal tissues. A particular gene that demonstrated at least a 2.5-fold increase in expression in prostate as compared to all tissues examined (each tissue evaluated individually) was considered to be prostate-specific/enriched. The tissues examined were adrenal gland, bladder, bone marrow, brain (amygdala, caudate nucleus, cerebellum, corpus callosum, hypothalamus, and thalamus), whole fetal brain, heart, kidney, liver (new cloning), lung, mammary gland, monocytes, peripheral blood lymphocytes, pituitary gland, placenta, pancreas, prostate, retina, spinal cord, salivary gland, small intestine, stomach, spleen, testis, thymus, trachea, thyroid, and uterus. This analysis identified 109 unique genes (with mpss signature sequence belonging to class 1-4, i.e. with confirmed match to cDNAs) whose expression was at least 2.5 fold that observed in other normal tissues. The list of prostate-specific/enriched genes is provided in Tables 5A-5D with the expression level in tpm in prostate shown. This list includes KLK2, KLK3, KLK4, TMPRSS2, which are genes previously shown to be prostate-specific.

TABLE 5A PROSTATE ENRICHED GENES IDENTIFIED BY RATIO SCHEMA (RATIO >2.5)* MPSS Sig. SEQ ID Genbank Genbank SEQ ID Tissue Names MPSS Signature NO: Name Accession No. NOs: Description GATCTCAGAACAACCTT 1688 DHRS7 BC000637 1797-1798 Dehydrogenase/reductase (SDR family) member 7 GATCCAGCCCAGAGACA 1689 NPY BC029497 1799-1800 Neuropeptide Y GATCACTCCTTATTTGC 1690 FLJ20010 AW172826 1801 Hypothetical protein FLJ20010 GATCCCTCTCCTCTCTG 1691 C9orf61 BI771919 1802 Chromosome 9 open reading frame 61 GATCTGACTTTTTACTT 1692 Lrp2bp BU853306 1803 Ankyrin repeat domain 37 GATCGTTAGCCTCATAT 1693 HOXB13 BC007092 1804-1805 Homeo box B13 GATCACAAGGAATCCTG 1694 CREB3L4 BC038962 1806-1807 CAMP responsive element binding protein 3-like 4 GATCTCATGGATGATTA 1695 LEPREL1 BC005029 1808-1809 Leprecan-like 1 GATCCAGAAATAAAGTC 1696 KLK4 CB051271 1810 Kallikrein 4 (prostase, enamel matrix, prostate) GATCTCACAGAAGATGT 1697 MGC35558 NM_145013 1811-1812 Chromosome 11 open reading frame 45 GATCCAAAATCACCAAG 1698 HAX1 BU157155 1813 HCLS1 associated protein X-1 GATCCTGGGCTGGAAGG 1699 0 AW207206 1814 Hypothetical gene supported by AY338954 GATCCAGATGCAGGACT 1700 0 BC013389 1815 LOC440156 GATCTGTGCTCATCTGT 1701 TMEM16G BC028162 1816-1817 Transmembrane protein 16G GATCATTTTATATCAAT 1702 MGC31963 BX099160 1818 Chromosome 1 open reading frame 85 GATCCACACTGAGAGAG 1703 KLK3 BC005307 1819-1820 Kallikrein 3, (prostate specific antigen) GATCCGTCTGTGCACAT 1704 TMPRSS2 NM_005656 1821-1822 Transmembrane protease, serine 2 GATCATTGTAGGGTAAC 1705 LOC221442 BC026923 1823 Hypothetical protein LOC221442 GATCAGCCCTCAAAAAA 1706 ARL10C BU159800 1824 ADP-ribosylation factor-like 8B GATCTGGATTCAGGACC 1707 MGC13102 NM_032323 1825-1826 Hypothetical protein MGC13102 GATCAAAAATAAAATGT 1708 0 AI954252 1827 Hypothetical gene supported by AK022914; AK095211; BC016035; BC041856; BX248778 GATCCGCTCTGGTCAAC 1709 SEPX1 BQ941313 1828 Selenoprotein X, 1 GATCCCTCAAGACTGGT 1710 ACPP BC007460 1829-1830 Acid phosphatase, prostate GATCCACAAAGACGAGG 1711 BIN3 BI911790 1831 Bridging integrator 3 GATCTCTCTGCGTTTGA 1712 SPON2 BC002707 1832-1833 Spondin 2, extracellular matrix protein GATCTCAACCTCGCTTG 1713 0 AK026938 1834 Hypothetical gene supported by AL713796 GATCAAGTTCCCGCTGG 1714 RPL18A BG818587 1835 Ribosomal protein L18a GATCATAATGAGGTTTG 1715 ABCC4 NM_005845 1836-1837 ATP-binding cassette, sub- family C (CFTR/MRP), member 4 GATCGGTGACATCGTAA 1716 RPS11 AA888242 1838 Ribosomal protein S11 GATCCACCAGCTGATAA 1717 NSEP1 CN353139 1839 Y box binding protein 1 GATCAACACACTTTATT 1718 FLJ22955 AA256381 1840 Hypothetical protein FLJ22955 GATCCCTTCCTTCCTCT 1719 HOXD11 AA513505 1841 Homeo box D11 GATCAGGACACAGACTT 1720 ORM1 BG564253 1842 Orosomucoid 1 GATCCTGCAATCTTGTA 1721 HTPAP AI572087 1843 Phosphatidic acid phosphatase type 2 domain containing 1B GATCCTCCTATGTTGTT 1722 KLK2 AA259243 1844 Kallikrein 2, prostatic GATCTGTACCTTGGCTA 1723 SLC2A12 AI675682 1845 Solute carrier family 2 (facilitated glucose transporter), member 12 GATCGGGGCAAGAGAGG 1724 NDRG1 NM_006096 1846-1847 N-myc downstream regulated gene 1 GATCCCCTCCCCTCCCC 1725 NPR1 NM_000906 1848-1849 Natriuretic peptide receptor A/guanylate cyclase A (atrionatriuretic peptide receptor A) GATCCTACAAAGAAGGA 1726 FLJ21511 NM_025087 1850-1851 Hypothetical protein FLJ21511 GATCATTTGCAGTTAAG 1727 FOXA1 NM_004496 1852-1853 Forkhead box A1 GATCTGTCTCCTGCTCT 1728 ENPP3 AI535878 1854 Ectonucleotide pyrophosphatase/phosphodiesterase 3 GATCCTTCCCAAGGTAC 1729 GATA2 NM_032638 1855-1856 GATA binding protein 2 GATCTTGTTGAAGTCAA 1730 ARG2 BX331427 1857 Arginase, type II GATCGCACCACTGTACA 1731 XPO1 AI569484 1858 Exportin 1 (CRM1 homolog, yeast) GATCATTTTCTGCTTTA 1732 ASB3 BC009569 1859-1860 Ankyrin repeat and SOCS box- containing 3 GATCCCCACACTTGTCC 1733 0 AK000028 1861 Hypothetical LOC90024 GATCTGGAATTGTCATA 1734 KLF3 BX100634 1862 Kruppel-like factor 3 (basic) GATCAATAAGCTTTAAA 1735 TGM4 BC007003 1863-1864 Transglutaminase 4 (prostate) GATCAATGTTTGTAGAT 1736 FLJ16231 NM_001008401 1865-1866 FLJ16231 protein GATCTACATGTCTATCA 1737 BLNK BX113323 1867 B-cell linker GATCTGTTTTAAATGAG 1738 SLC14A1 NM_015865 1868-1869 Solute carrier family 14 (urea transporter), member 1 (Kidd blood group) GATCAAAAAATGCTGCA 1739 PTPLB AI017286 1870 Protein tyrosine phosphatase- like (proline instead of catalytic arginine), member b GATCATGTCTTCATTTT 1740 OR51E2 NM_030774 1871-1872 Olfactory receptor, family 51, subfamily E, member 2 GATCCCTCCACCCCCAT 1741 FAAH NM_001441 1873-1874 Fatty acid amide hydrolase GATCCTAAGCCATAAAT 1742 STAT6 AL044554 1875 Signal transducer and activator of transcription 6, interleukin-4 induced GATCATCGTCCTCATCG 1743 ANKH CB049466 1876 Ankylosis, progressive homolog (mouse) GATCATCATTTGTCATT 1744 DSCR1L2 AW575747 1877 Down syndrome critical region gene 1-like 2 GATCTAATTTGAAAAAC 1745 TRPM8 NM_024080 1878-1879 Transient receptor potential cation channel, subfamily M, member 8 GATCTTCCTTGTATCAT 1746 TMC4 AV724505 1880 Transmembrane channel-like 4 GATCTCCCCCATGCCTG 1747 ZNF589 BC005859 1881-1882 Zinc finger protein 589 GATCAAATTTAGTATTT 1748 LRRK1 BC005408 1883-1884 Leucine-rich repeat kinase 1 GATCTGCCTTATAAACA 1749 STEAP2 AA177004 1885 Six transmembrane epithelial antigen of the prostate 2 GATCAGAAAATGAGCTC 1750 SAFB2 BC001216 1886 Scaffold attachment factor B2 GATCACCGTGGAGGTTA 1751 CPE BG707154 1887 Carboxypeptidase E GATCCCTCTGTGCTTCT 1752 GNB2L1 AA024878 1888 Guanine nucleotide binding protein (G protein), beta polypeptide 2-like 1 GATCTCATTTTTAGAGC 1753 LOC92689 BU688574 1889 Hypothetical protein BC001096 GATCATCACATTTCGTG 1754 DLG1 BC042118 1890 Discs, large homolog 1 (Drosophila) GATCATTTTCTGCTTCA 1755 SEMG1 NM_003007 1891-1892 Semenogelin I GATCAATGAAGGAGAGA 1756 SPATA13 BM875598 1893 Spermatogenesis associated 13 GATCCCAACTACTCGGG 1757 LOC157657 NM_177965 1894-1895 Chromosome 8 open reading frame 37 GATCAGTTTTTCTGTAA 1758 KIAA1411 CA433208 1896 KIAA1411 GATCAAAATTTTAAAAA 1759 MGC20781 BM984931 1897 5′-nucleotidase, cytosolic III-like GATCACCCTTCTCTTCC 1760 LOC255189 BC035335 1898-1899 Phospholipase A2, group IVF GATCCTGGGTACTGAAA 1761 ERBB2 BC080193 1900 V-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian) GATCGTTCTAAGAGTGT 1762 ZFP64 NM_199427 1901-1902 Zinc finger protein 64 homolog (mouse) GATCATCATCAAGGGCT 1763 SUHW2 BC042370 1903 Suppressor of hairy wing homolog 2 (Drosophila) GATCAAAATGATTTTCA 1764 ELOVL7 AL137506 1904-1905 ELOVL family member 7, elongation of long chain fatty acids (yeast) GATCTGATTTTTTTCCC 1765 TRAF4 AI888175 1906 TNF receptor-associated factor 4 GATCCCATTTCTCACCC 1766 SLC39A2 AI669751 1907 Solute carrier family 39 (zinc transporter), member 2 GATCCTCCCGCCTTGCC 1767 HNF4G AI088739 1908 Hepatocyte nuclear factor 4, gamma GATCTTTCTTTTTTTGT 1768 SLC22A3 BC070300 1909 Solute carrier family 22 (extraneuronal monoamine transporter), member 3 GATCTTAACTGTCTCCT 1769 HIST2H2BE BC005827 1910 Histone 2, H2be GATCAGTTTGATTCTGT 1770 AMD1 BC041345 1911-1912 Adenosylmethionine decarboxylase 1 GATCATGATGTAGAGGG 1771 TYMS BX390036 1913 Thymidylate synthetase GATCGCACCACTACAGT 1772 PHC3 AK022455 1914 Polyhomeotic like 3 (Drosophila) GATCTCAAAGTGCCTTC 1773 SARG AL832940 1915-1916 Chromosome 1 open reading frame 116 GATCAATGTCAAACTTC 1774 MTERF BC000965 1917-1918 Mitochondrial transcription termination factor GATCTCCCAGAGTCTAA 1775 CYP4F8 NM_007253 1919-1920 Cytochrome P450, family 4, subfamily F, polypeptide 8 GATCCTGATGGCTGTGT 1776 PPAP2A AK124401 1921 Phosphatidic acid phosphatase type 2A GATCACTTCCCGCAGTC 1777 KIAA0056 BC011408 1922-1923 KIAA0056 protein GATCTCAAAGGAACCAA 1778 MSMB AA469293 1924 Microseminoprotein, beta- GATCTGTGCCAGGGTTA 1779 VEGF AK056914 1925 Vascular endothelial growth factor GATCTCTTTTTATTTAA 1780 CDH1 NM_004360 1926-1927 Cadherin 1, type 1, E-cadherin (epithelial) GATCTCCAGCACCAATC 1781 TARP BC062761 1928-1929 TCR gamma alternate reading frame protein GATCTGGCGCTTGGGGG 1782 RFP2 NM_001007278 1930-1931 Ret finger protein 2 GATCCCGACGGGGGCAT 1783 MESP1 NM_018670 1932-1933 Mesoderm posterior 1 homolog (mouse) GATCCCGGGCCGTTATC 1784 TRPM4 AA026974 1934 Transient receptor potential cation channel, subfamily M, member 4 GATCTTTCTCAAAATAT 1785 PAK1IP1 AI468032 1935 PAK1 interacting protein 1 GATCGTGACGCTTAATA 1786 HNRPA1 CF122297 1936 Heterogeneous nuclear ribonucleoprotein A1 GATCGCATAATTTTTAA 1787 ZNF207 CB053869 1937 Zinc finger protein 207 GATCCCAACACTGAAGG 1788 WNK4 NM_032387 1938-1939 WNK lysine deficient protein kinase 4 GATCTTAAAAACTGCAG 1789 APXL2 BQ448015 1940 Apical protein 2 GATCATTTTTTCTATCA 1790 MED28 AI554477 1941 Mediator of RNA polymerase II transcription, subunit 28 homolog (yeast) GATCCCATTGTGTGTAT 1791 LOC285300 AK095655 1942 Hypothetical protein LOC285300 GATCTCAAAGGAAAAAA 1792 0 AW291753 1943 Transcribed locus GATCTTCTGTTATATTT 1793 0 BM023121 1944 Full length insert cDNA clone ZD79H10 GATCCACAACATACAGC 1794 0 AY338953 1945 Prostate-specific P712P mRNA sequence GATCTGTGCAGTTGTAA 1795 0 AY533562 1946 KLK16 mRNA, partial sequence GATCTACTATGCCAAAT 1796 0 BC030554 1947 (clone HGT25) T cell receptor gamma-chain mRNA, V region *ratio of prostate expression in tpm to other organs greater than 2.5

TABLE 5B PROSTATE ENRICHED GENES IDENTIFIED BY RATIO SCHEMA (RATIO > 2.5)* SignalP3.0 Genbank prediction Genbank SEQ ID SignalP3.0 prediction Signal peptide Accession No. NOs: Name Prediction probability BC000637 1797-1798 DHRS7 Signal peptide 0.999 BC029497 1799-1800 NPY Signal peptide 0.998 AW172826 1801 FLJ20010 Non-secretory protein 0.001 BI771919 1802 C9orf61 Signal peptide 0.994 BU853306 1803 Lrp2bp Non-secretory protein 0 BC007092 1804-1805 HOXB13 Non-secretory protein 0 BC038962 1806-1807 CREB3L4 Non-secretory protein 0 BC005029 1808-1809 LEPREL1 Signal peptide 0.995 CB051271 1810 KLK4 Signal peptide 0.988 NM_145013 1811-1812 MGC35558 Signal peptide 0.935 BU157155 1813 HAX1 Non-secretory protein 0.001 AW207206 1814 0 Non-secretory protein 0.001 BC013389 1815 0 Non-secretory protein 0 BC028162 1816-1817 TMEM16G Non-secretory protein 0.001 BX099160 1818 MGC31963 Signal peptide 0.994 BC005307 1819-1820 KLK3 Signal peptide 0.992 NM_005656 1821-1822 TMPRSS2 Non-secretory protein 0 BC026923 1823 LOC221442 Signal anchor 0.01 BU159800 1824 ARL10C Non-secretory protein 0 NM_032323 1825-1826 MGC13102 Non-secretory protein 0 AI954252 1827 0 Non-secretory protein 0.128 BQ941313 1828 SEPX1 Non-secretory protein 0 BC007460 1829-1830 ACPP Signal peptide 1 BI911790 1831 BIN3 Non-secretory protein 0 BC002707 1832-1833 SPON2 Signal peptide 0.998 AK026938 1834 0 Signal peptide 0.587 BG818587 1835 RPL18A Non-secretory protein 0 NM_005845 1836-1837 ABCC4 Non-secretory protein 0 AA888242 1838 RPS11 Non-secretory protein 0 CN353139 1839 NSEP1 Non-secretory protein 0.001 CA256381 1840 FLJ22955 Non-secretory protein 0.06 AA513505 1841 HOXD11 Non-secretory protein 0 BG564253 1842 ORM1 Signal peptide 1 AI572087 1843 HTPAP Non-secretory protein 0.021 AA259243 1844 KLK2 Signal peptide 0.985 AI675682 1845 SLC2A12 Non-secretory protein 0 NM_006096 1846-1847 NDRG1 Non-secretory protein 0 NM_000906 1848-1849 NPR1 Signal peptide 0.997 NM_025087 1850-1851 FLJ21511 Non-secretory protein 0.005 NM_004496 1852-1853 FOXA1 Non-secretory protein 0 AI535878 1854 ENPP3 Non-secretory protein 0.069 NM_032638 1855-1856 GATA2 Non-secretory protein 0 BX331427 1857 ARG2 Non-secretory protein 0.014 AI569484 1858 XPO1 Non-secretory protein 0 BC009569 1859-1860 ASB3 Non-secretory protein 0 AK000028 1861 0 Non-secretory protein 0.001 BX100634 1862 KLF3 Non-secretory protein 0 BC007003 1863-1864 TGM4 Non-secretory protein 0 NM_001008401 1865-1866 FLJ16231 Non-secretory protein 0 BX113323 1867 BLNK Non-secretory protein 0 NM_015865 1868-1869 SLC14A1 Non-secretory protein 0 AI017286 1870 PTPLB Non-secretory protein 0.06 NM_030774 1871-1872 OR51E2 Non-secretory protein 0.008 NM_001441 1873-1874 FAAH Signal peptide 0.805 AL044554 1875 STAT6 Non-secretory protein 0 CB049466 1876 ANKH Non-secretory protein 0.001 AW575747 1877 DSCR1L2 Non-secretory protein 0 NM_024080 1878-1879 TRPM8 Non-secretory protein 0 AV724505 1880 TMC4 Non-secretory protein 0 BC005859 1881-1882 ZNF589 Non-secretory protein 0 BC005408 1883-1884 LRRK1 Non-secretory protein 0 AA177004 1885 STEAP2 Non-secretory protein 0 BC001216 1886 SAFB2 Non-secretory protein 0 BG707154 1887 CPE Signal peptide 1 AA024878 1888 GNB2L1 Non-secretory protein 0 BU688574 1889 LOC92689 Non-secretory protein 0 BC042118 1890 DLG1 Non-secretory protein 0 NM_003007 1891-1892 SEMG1 Signal peptide 0.922 BM875598 1893 SPATA13 Non-secretory protein 0 NM_177965 1894-1895 LOC157657 Non-secretory protein 0 CA433208 1896 KIAA1411 Non-secretory protein 0 BM984931 1897 MGC20781 Non-secretory protein 0 BC035335 1898-1899 LOC255189 Non-secretory protein 0 BC080193 1900 ERBB2 Non-secretory protein 0 NM_199427 1901-1902 ZFP64 Non-secretory protein 0 BC042370 1903 SUHW2 Non-secretory protein 0 AL137506 1904-1905 ELOVL7 Non-secretory protein 0 AI888175 1906 TRAF4 Non-secretory protein 0 AI669751 1907 SLC39A2 Signal peptide 0.982 AI088739 1908 HNF4G Non-secretory protein 0.001 BC070300 1909 SLC22A3 Signal anchor 0.097 BC005827 1910 HIST2H2BE Non-secretory protein 0 BC041345 1911-1912 AMD1 Non-secretory protein 0 BX390036 1913 TYMS Non-secretory protein 0 AK022455 1914 PHC3 Non-secretory protein 0 AL832940 1915-1916 SARG Non-secretory protein 0 BC000965 1917-1918 MTERF Non-secretory protein 0 NM_007253 1919-1920 CYP4F8 Signal peptide 1 AK124401 1921 PPAP2A Non-secretory protein 0.348 BC011408 1922-1923 KIAA0056 Non-secretory protein 0 AA469293 1924 MSMB Signal peptide 0.997 AK056914 1925 VEGF Non-secretory protein 0 NM_004360 1926-1927 CDH1 Signal peptide 0.896 BC062761 1928-1929 TARP Non-secretory protein 0 NM_001007278 1930-1931 RFP2 Non-secretory protein 0 NM_018670 1932-1933 MESP1 Signal anchor 0.004 AA026974 1934 TRPM4 Non-secretory protein 0 AI468032 1935 PAK1IP1 Non-secretory protein 0.001 CF122297 1936 HNRPA1 Non-secretory protein 0 CB053869 1937 ZNF207 Non-secretory protein 0 NM_032387 1938-1939 WNK4 Non-secretory protein 0 BQ448015 1940 APXL2 Non-secretory protein 0 AI554477 1941 MED28 AK095655 1942 LOC285300 AW291753 1943 0 BM023121 1944 0 AY338953 1945 0 AY533562 1946 0 BC030554 1947 0 *ratio of prostate expression in tpm to other organs greater than 2.5

TABLE 5C PROSTATE ENRICHED GENES IDENTIFIED BY RATIO SCHEMA (RATIO > 2.5)* SecretomeP2.0 TMHMM 2.0 SignalP3.0 prediction prediction Genbank prediction Secreted Pred trans- Genbank SEQ ID Max cleavage potential membrane Accession No. NOs: name site probability (Odds) domains BC000637 1797-1798 DHRS7 0.599 between 6.3 1 pos. 28 and 29 BC029497 1799-1800 NPY 0.520 between 6.09 1 pos. 28 and 29 AW172826 1801 FLJ20010 0.000 between 6.06 0 pos. 46 and 47 BI771919 1802 C9orf61 0.534 between 5.9 2 pos. 29 and 30 BU853306 1803 Lrp2bp 0.000 between 5.62 0 pos. 55 and 56 BC007092 1804-1805 HOXB13 0.000 between 5.14 0 pos. −1 and 0 BC038962 1806-1807 CREB3L4 0.000 between 4.72 0 pos. −1 and 0 BC005029 1808-1809 LEPREL1 0.991 between 4.59 0 pos. 24 and 25 CB051271 1810 KLK4 0.401 between 4.57 1 pos. 29 and 30 NM_145013 1811-1812 MGC35558 0.901 between 4.47 0 pos. 22 and 23 BU157155 1813 HAX1 0.001 between 4.41 0 pos. 18 and 19 AW207206 1814 0 0.001 between 4.39 0 pos. 20 and 21 BC013389 1815 0 0.000 between 4.3 0 pos. 27 and 28 BC028162 1816-1817 TMEM16G 0.001 between 4.29 7 pos. 22 and 23 BX099160 1818 MGC31963 0.855 between 4.22 2 pos. 35 and 36 BC005307 1819-1820 KLK3 0.525 between 3.938 0 pos. 23 and 24 NM_005656 1821-1822 TMPRSS2 0.000 between 3.86 1 pos. −1 and 0 BC026923 1823 LOC221442 0.004 between 3.81 0 pos. 50 and 51 BU159800 1824 ARL10C 0.000 between 3.76 0 pos. 35 and 36 NM_032323 1825-1826 MGC13102 0.000 between 3.69 5 pos. −1 and 0 AI954252 1827 0 0.121 between 3.58 0 pos. 42 and 43 BQ941313 1828 SEPX1 0.000 between 3.49 0 pos. 13 and 14 BC007460 1829-1830 ACPP 0.975 /between 3.49 1 pos. 32 and 33 BI911790 1831 BIN3 0.000 between 3.41 0 pos. −1 and 0 BC002707 1832-1833 SPON2 0.829 between 3.06 0 pos. 26 and 27 AK026938 1834 0 0.568 between 3.02 0 pos. 27 and 28 BG818587 1835 RPL18A 0.000 between 2.8 0 pos. 24 and 25 NM_005845 1836-1837 ABCC4 0.000 between 2.67 11 pos. −1 and 0 AA888242 1838 RPS11 0.000 between 2.64 0 pos. −1 and 0 CN353139 1839 NSEP1 0.000 between 2.35 0 pos. 25 and 26 AA256381 1840 FLJ22955 0.038 between 2.19 1 pos. 15 and 16 AA513505 1841 HOXD11 0.000 between 2.14 0 pos. 20 and 21 BG564253 1842 ORM1 0.923 between 2.03 0 pos. 18 and 19 AI572087 1843 HTPAP 0.009 between 2.01 4 pos. 63 and 64 AA259243 1844 KLK2 0.455 between 1.81 0 pos. 17 and 18 AI675682 1845 SLC2A12 0.000 between 1.79 12 pos. 51 and 52 NM_006096 1846-1847 NDRG1 0.000 between 1.76 0 pos. −1 and 0 NM_000906 1848-1849 NPR1 0.960 between 1.75 0 pos. 32 and 33 NM_025087 1850-1851 FLJ21511 0.005 between 1.75 10 pos. 20 and 21 NM_004496 1852-1853 FOXA1 0.000 between 1.71 0 pos. −1 and 0 AI535878 1854 ENPP3 0.036 between 1.69 1 pos. 42 and 43 NM_032638 1855-1856 GATA2 0.000 between 1.65 0 pos. 22 and 23 BX331427 1857 ARG2 0.013 between 1.56 0 pos. 36 and 37 AI569484 1858 XPO1 0.000 between 1.54 0 pos. −1 and 0 BC009569 1859-1860 ASB3 0.000 between 1.53 0 pos. −1 and 0 AK000028 1861 0 0.000 between 1.46 0 pos. 22 and 23 BX100634 1862 KLF3 0.000 between 1.4 0 pos. −1 and 0 BC007003 1863-1864 TGM4 0.000 between 1.36 0 pos. −1 and 0 NM_001008401 1865-1866 FLJ16231 0.000 between 1.21 0 pos. −1 and 0 BX113323 1867 BLNK 0.000 between 1.21 0 pos. −1 and 0 NM_015865 1868-1869 SLC14A1 0.000 between 1.2 8 pos. −1 and 0 AI017286 1870 PTPLB 0.028 between 1.2 4 pos. 63 and 64 NM_030774 1871-1872 OR51E2 0.003 between 1.2 7 pos. 22 and 23 NM_001441 1873-1874 FAAH 0.549 between 1.2 1 pos. 28 and 29 AL044554 1875 STAT6 0.000 between 1.17 0 pos. −1 and 0 CB049466 1876 ANKH 0.000 between 1.15 8 pos. 26 and 27 AW575747 1877 DSCR1L2 0.000 between 1.12 0 pos. −1 and 0 NM_024080 1878-1879 TRPM8 0.000 between 1.07 8 pos. −1 and 0 AV724505 1880 TMC4 0.000 between 1.06 8 pos. −1 and 0 BC005859 1881-1882 ZNF589 0.000 between 0.99 1 pos. −1 and 0 BC005408 1883-1884 LRRK1 0.000 between 0.99 0 pos. −1 and 0 AA177004 1885 STEAP2 0.000 between 0.95 6 pos. −1 and 0 BC001216 1886 SAFB2 0.000 between 0.95 0 pos. −1 and 0 BG707154 1887 CPE 0.859 between 0.93 0 pos. 27 and 28 AA024878 1888 GNB2L1 0.000 between 0.92 0 pos. 33 and 34 BU688574 1889 LOC92689 0.000 between 0.91 0 pos. −1 and 0 BC042118 1890 DLG1 0.000 between 0.87 0 pos. −1 and 0 NM_003007 1891-1892 SEMG1 0.515 between 0.85 0 pos. 23 and 24 BM875598 1893 SPATA13 0.000 between 0.81 0 pos. −1 and 0 NM_177965 1894-1895 LOC157657 0.000 between 0.81 0 pos. −1 and 0 CA433208 1896 KIAA1411 0.000 between 0.8 0 pos. −1 and 0 BM984931 1897 MGC20781 0.000 between 0.79 0 pos. 25 and 26 BC035335 1898-1899 LOC255189 0.000 between 0.78 0 pos. 23 and 24 BC080193 1900 ERBB2 0.000 between 0.74 2 pos. −1 and 0 NM_199427 1901-1902 ZFP64 0.000 between 0.68 0 pos. −1 and 0 BC042370 1903 SUHW2 0.000 between 0.67 0 pos. −1 and 0 AL137506 1904-1905 ELOVL7 0.000 between 0.67 7 pos. −1 and 0 AI888175 1906 TRAF4 0.000 between 0.63 0 pos. −1 and 0 AI669751 1907 SLC39A2 0.297 between 0.62 8 pos. 23 and 24 AI088739 1908 HNF4G 0.001 between 0.59 0 pos. 21 and 22 BC070300 1909 SLC22A3 0.048 between 0.58 12 pos. 33 and 34 BC005827 1910 HIST2H2BE 0.000 between 0.58 0 pos. −1 and 0 BC041345 1911-1912 AMD1 0.000 between 0.58 0 pos. −1 and 0 BX390036 1913 TYMS 0.000 between 0.57 0 pos. −1 and 0 AK022455 1914 PHC3 0.000 between 0.57 0 pos. −1 and 0 AL832940 1915-1916 SARG 0.000 between 0.56 0 pos. 21 and 22 BC000965 1917-1918 MTERF 0.000 between 0.56 0 pos. 14 and 15 NM_007253 1919-1920 CYP4F8 0.781 between 0.56 1 pos. 36 and 37 AK124401 1921 PPAP2A 0.226 between 0.53 5 pos. 30 and 31 BC011408 1922-1923 KIAA0056 0.000 between 0.52 0 pos. −1 and 0 AA469293 1924 MSMB 0.928 between 0.51 1 pos. 20 and 21 AK056914 1925 VEGF 0.000 between 0.485 0 pos. −1 and 0 NM_004360 1926-1927 CDH1 0.487 between 0.36 1 pos. 22 and 23 BC062761 1928-1929 TARP 0.000 between 0.35 1 pos. 20 and 21 NM_001007278 1930-1931 RFP2 0.000 between 0.32 1 pos. 24 and 25 NM_018670 1932-1933 MESP1 0.002 between 0.31 0 pos. 20 and 21 AA026974 1934 TRPM4 0.000 between 0.3 5 pos. −1 and 0 AI468032 1935 PAK1IP1 0.000 between 0.27 0 pos. 25 and 26 CF122297 1936 HNRPA1 0.000 between 0.22 0 pos. 32 and 33 CB053869 1937 ZNF207 0.000 between 0.21 0 pos. −1 and 0 NM_032387 1938-1939 WNK4 0.000 between 0.2 0 pos. −1 and 0 BQ448015 1940 APXL2 0.000 between 0.19 0 pos. 41 and 42 AI554477 1941 MED28 #N/A #N/A AK095655 1942 LOC285300 #N/A #N/A AW291753 1943 0 #N/A #N/A BM023121 1944 0 #N/A #N/A AY338953 1945 0 #N/A #N/A AY533562 1946 0 #N/A #N/A BC030554 1947 0 #N/A #N/A *ratio of prostate expression in tpm to other organs greater than 2.5

TABLE 5D PROSTATE ENRICHED GENES IDENTIFIED BY RATIO SCHEMA (RATIO > 2.5)* Genbank Prostate Genbank SEQ ID Expression Accession No. NOs: name NN-score Odds (tmp) BC000637 1797-1798 DHRS7 0.92 6.302 754 BC029497 1799-1800 NPY 0.911 6.099 642 AW172826 1801 FLJ20010 0.911 6.061 92 BI771919 1802 C9orf61 0.906 5.902 91 BU853306 1803 Lrp2bp 0.895 5.626 95 BC007092 1804-1805 HOXB13 0.875 5.145 344 BC038962 1806-1807 CREB3L4 0.866 4.721 334 BC005029 1808-1809 LEPREL1 0.857 4.594 118 CB051271 1810 KLK4 0.856 4.575 360 NM_145013 1811-1812 MGC35558 0.86 4.477 53 BU157155 1813 HAX1 0.854 4.412 67 AW207206 1814 0 0.854 4.391 279 BC013389 1815 0 0.85 4.304 64 BC028162 1816-1817 TMEM16G 0.843 4.293 281 BX099160 1818 MGC31963 0.846 4.222 53 BC005307 1819-1820 KLK3 0.838 3.938 24771 NM_005656 1821-1822 TMPRSS2 0.816 3.861 1425 BC026923 1823 LOC221442 0.8 3.812 104 BU159800 1824 ARL10C 0.822 3.76 167 NM_032323 1825-1826 MGC13102 0.788 3.699 238 AI954252 1827 0 0.814 3.589 159 BQ941313 1828 SEPX1 0.798 3.492 56 BC007460 1829-1830 ACPP 0.815 3.495 55 BI911790 1831 BIN3 0.806 3.41 54 BC002707 1832-1833 SPON2 0.766 3.063 873 AK026938 1834 0 0.769 3.025 304 BG818587 1835 RPL18A 0.768 2.806 58 NM_005845 1836-1837 ABCC4 0.747 2.671 454 AA888242 1838 RPS11 0.754 2.645 50 CN353139 1839 NSEP1 0.733 2.358 179 CA256381 1840 FLJ22955 0.688 2.196 57 AA513505 1841 HOXD11 0.715 2.142 99 BG564253 1842 ORM1 0.691 2.034 180 AI572087 1843 HTPAP 0.677 2.013 332 AA259243 1844 KLK2 0.676 1.816 7988 AI675682 1845 SLC2A12 0.499 1.792 127 NM_006096 1846-1847 NDRG1 0.667 1.765 2688 NM_000906 1848-1849 NPR1 0.658 1.755 150 NM_025087 1850-1851 FLJ21511 0.605 1.756 230 NM_004496 1852-1853 FOXA1 0.627 1.711 793 AI535878 1854 ENPP3 0.635 1.693 54 NM_032638 1855-1856 GATA2 0.598 1.659 238 BX331427 1857 ARG2 0.621 1.56 150 AI569484 1858 XPO1 0.604 1.54 68 BC009569 1859-1860 ASB3 0.607 1.538 2781 AK000028 1861 0 0.595 1.466 55 BX100634 1862 KLF3 0.581 1.401 136 BC007003 1863-1864 TGM4 0.59 1.368 5602 NM_001008401 1865-1866 FLJ16231 0.55 1.21 254 BX113323 1867 BLNK 0.559 1.211 183 NM_015865 1868-1869 SLC14A1 0.335 1.208 255 AI017286 1870 PTPLB 0.457 1.201 102 NM_030774 1871-1872 OR51E2 0.522 1.208 420 NM_001441 1873-1874 FAAH 0.535 1.206 476 AL044554 1875 STAT6 0.547 1.174 71 CB049466 1876 ANKH 0.335 1.153 58 AW575747 1877 DSCR1L2 0.471 1.123 225 NM_024080 1878-1879 TRPM8 0.519 1.077 267 AV724505 1880 TMC4 0.402 1.064 120 BC005859 1881-1882 ZNF589 0.491 0.992 156 BC005408 1883-1884 LRRK1 0.499 0.999 202 AA177004 1885 STEAP2 0.482 0.954 2156 BC001216 1886 SAFB2 0.427 0.954 76 BG707154 1887 CPE 0.464 0.933 148 AA024878 1888 GNB2L1 0.465 0.921 59 BU688574 1889 LOC92689 0.461 0.918 82 BC042118 1890 DLG1 0.457 0.872 50 NM_003007 1891-1892 SEMG1 0.447 0.853 4660 BM875598 1893 SPATA13 0.422 0.812 79 NM_177965 1894-1895 LOC157657 0.434 0.819 92 CA433208 1896 KIAA1411 0.427 0.809 69 BM984931 1897 MGC20781 0.417 0.795 117 BC035335 1898-1899 LOC255189 0.49 1.04 56 BC080193 1900 ERBB2 0.377 0.743 1770 NM_199427 1901-1902 ZFP64 0.374 0.688 80 BC042370 1903 SUHW2 0.364 0.678 587 AL137506 1904-1905 ELOVL7 0.322 0.673 256 AI888175 1906 TRAF4 0.343 0.631 50 AI669751 1907 SLC39A2 0.34 0.629 60 AI088739 1908 HNF4G 0.32 0.593 225 BC070300 1909 SLC22A3 0.294 0.581 77 BC005827 1910 HIST2H2BE 0.306 0.587 912 BC041345 1911-1912 AMD1 0.317 0.588 438 BX390036 1913 TYMS 0.306 0.571 67 AK022455 1914 PHC3 0.287 0.57 105 AL832940 1915-1916 SARG 0.302 0.563 158 BC000965 1917-1918 MTERF 0.3 0.56 190 NM_007253 1919-1920 CYP4F8 0.28 0.566 54 AK124401 1921 PPAP2A 0.211 0.533 75 BC011408 1922-1923 KIAA0056 0.281 0.527 287 AA469293 1924 MSMB 0.27 0.517 275 AK056914 1925 VEGF 0.256 0.485 202 NM_004360 1926-1927 CDH1 0.179 0.362 192 BC062761 1928-1929 TARP 0.174 0.353 564 NM_001007278 1930-1931 RFP2 0.162 0.322 192 NM_018670 1932-1933 MESP1 0.154 0.315 133 AA026974 1934 TRPM4 0.147 0.305 290 AI468032 1935 PAK1IP1 0.13 0.271 74 CF122297 1936 HNRPA1 0.106 0.228 104 CB053869 1937 ZNF207 0.099 0.212 72 NM_032387 1938-1939 WNK4 0.089 0.201 100 BQ448015 1940 APXL2 0.083 0.19 244 AI554477 1941 MED28 700 AK095655 1942 LOC285300 84 AW291753 1943 0 310 BM023121 1944 0 178 AY338953 1945 0 166 AY533562 1946 0 67 BC030554 1947 0 66 *ratio of prostate expression in tpm to other organs greater than 2.5

Additional analysis was carried out to determine the secretion potential of the prostate-specific genes identified. The analysis programs used included SignalP 3.0, Secretome 2.0 and TMHMM 2.0 (see http colon double slash www dot cbs dot dtu dot dk/services/). The SignalP analysis identifies classical secreted proteins and was conducted using the classical secretion pathway prediction as described at http colon double slash www dot cbs dot dtu dot dk/services/SignalP/ (see Jannick Dyrløv Bendtsen, et al. J. Mol. Biol., 340:783-795, 2004; Henrik Nielsen et al., Protein Engineering, 10:1-6, 1997; Henrik Nielsen and Anders Krogh. Proceedings of the Sixth International Conference on Intelligent Systems for Molecular Biology (ISMB 6), AAAI Press, Menlo Park, Calif., pp. 122-130, 1998). The Secretome2.0 analysis identifies nonclassical secreted proteins (see J. Dyrløv Bendtsen, et al., Protein Eng. Des. Sel., 17(4):349-356, 2004). TMHMM uses hidden Markov model for three-state (TM-helix, inside, outside) topology prediction of transmembrane proteins (see Erik L. L. Sonnhammer, et al., Proc. of Sixth Int. Conf. on Intelligent Systems for Molecular Biology, p. 175-182 Ed. J. Glasgow, T. Littlejohn, F. Major, R. Lathrop, D. Sankoff, and C. Sensen, Menlo Park, Calif.: AAAI Press, 1998). According to the SignalP analysis method, proteins with an odds scoring 3 or higher have a high confidence of being secreted. However, it should be noted that several proteins scoring well below 3 by this method are known to be secreted proteins detected in the blood (see e.g., Table 5, KLK2). Further, these analyses do not take into account proteins that may be shed.

In summary, this example identifies prostate-specific and potentially secreted prostate-specific proteins that can be used in diagnostic panels for the detection of diseases of the prostate.

Example 8 Prostate Cancer Diagnostics Using Multiparameter Analysis

This example describes a multiparameter diagnostic fingerprint using the NDRG1 prostate-specific protein in combination with PSA. The NDRG1 prostate-specific protein further improved prostate cancer detection when used in combination with PSA.

Commercially available antibodies specific for numerous proteins encoded by prostate-specific genes as described in Table 5 were used to determine which proteins would be useful in a multiparameter diagnostic assay for prostate cancer. Most of the commercially available antibodies were not suitable (e.g., were not sensitive enough or showed non-specific binding). However, the antibody available for NDRG1 (anti-NDRG1 (C terminal) poly IgY; Cat#A22272B; GenWay Inc) was shown to specifically bind to NDRG1 from serum. NDRG1 is a member of the N-myc downregulated gene (NDRG) family that belongs to the alpha/beta hydrolase superfamily. It is classified as a tumor suppressor and heavy metal-response protein. Its expression is modulated by diverse physiological and pathological conditions including hypoxia, cellular differentiation, heavy metal, N-myc and neoplasia (Lachat P, et al.; Histochem Cell Biol. 2002 November; 118 (5):399-408).

NDRG1 protein expression was analyzed in serum samples from 18 advanced prostate cancer patients, 21 prostate cancer patients with localized cancer, and 22 normal controls. Western blot analysis was used to measure serum protein expression as follows: Serum was diluted (1:10) with lysis buffer (50 mM Hepes, pH 7.4, 4 mM EDTA, 2 mM EGTA, 2 μM PMSF, 20 μg/ml, leupeptine (or 1× protease inhibitor cocktail), 1 mM Na₃VO₄,10 mM NaF, 2 mM Na pyrophosphate, 1% Triton X-100). Protein concentration was determined using the Bio-Rad protein assay kit. Serum proteins (50 μg) were subjected to SDS-PAGE electrophoresis and transferred to a PVDF membrane (Hybond-P, Amersham Pharmacia Biotech, Piscataway, N.J.). The membrane was blocked with 4% non-fat milk in TBS (25 mM Tris, pH 7.4, 125 mM NaCl) for 1 h at room temperature, followed by incubation with primary antibodies against NDRG1 IgY (1:500) overnight at 4° C. The membranes were washed 3 times with TBS, and then incubated with horseradish peroxidase conjugated anti-rabbit IgY (1:16,000) for 1 h. The immunoblot was then washed five times with TBS and developed using an ECL (Amersham). The intensities of the single band corresponding to the NDRG1 protein were then scored. The results are summarized in Table 6 together with serum PSA measurements performed using a commercial ELISA kit.

TABLE 6 COMBINED ANALYSIS OF NDRG1 AND PSA SERUM EXPRESSION INCREASES PROSTATE CANCER DIAGNOSIS CONFIDENCE. NDRG-1 PSA cancer intensity values serum diagnosis serum diagnosis status (scores*) (ng/ml) by PSA by NDRG1 Advanced 3 70.48 identified as identified as cancer cancer by PSA by NDRG1 assay assay Advanced 4 127.3 identified as identified as cancer cancer by PSA by NDRG1 assay assay Advanced 4 422.1 identified as identified as cancer cancer by PSA by NDRG1 assay assay Advanced 4 1223 identified as identified as cancer cancer by PSA by NDRG1 assay assay Advanced 4 71.28 identified as identified as cancer cancer by PSA by NDRG1 assay assay Advanced 2 133.2 identified as missed byNDRG1 cancer by PSA assay assay Advanced 4 353.7 identified as identified as cancer cancer by PSA by NDRG1 assay assay Advanced 1 73.95 identified as missed byNDRG1 cancer by PSA assay assay Advanced 3 454.8 identified as identified as cancer cancer by PSA by NDRG1 assay assay Advanced 4 474 identified as identified as cancer cancer by PSA by NDRG1 assay assay Advanced 6 150.1 identified as identified as cancer cancer by PSA by NDRG1 assay assay Advanced 0 1375 identified as missed byNDRG1 cancer by PSA assay assay Advanced 6 71.28 identified as identified as cancer cancer by PSA by NDRG1 assay assay Advanced 6 4066 identified as identified as cancer cancer by PSA by NDRG1 assay assay Advanced 4 1199 identified as identified as cancer cancer by PSA by NDRG1 assay assay Advanced 1 38.14 identified as missed byNDRG1 cancer by PSA assay assay Advanced 6 552.6 identified as identified as cancer cancer by PSA by NDRG1 assay assay Advanced 5 321 identified as identified as cancer cancer by PSA by NDRG1 assay assay Primary −1 14.2 possibly cancer Primary 2 6.27 Grey Zone of diagnosis by Psa Primary 2 9.2 Grey Zone of diagnosis by Psa Primary 1 8.57 Grey Zone of diagnosis by Psa Primary 0 5.67 Grey Zone of diagnosis by Psa Primary 2 11.3 possibly cancer Primary 0 4.58 Grey Zone of diagnosis by Psa Primary 0 5.67 Grey Zone of diagnosis by Psa Primary −1 6.48 Grey Zone of diagnosis by Psa Primary 3 12.71 possibly cancer strong NDRG-1 expression reinforces the diagnosis of this patient as cancer Primary 3 4.93 Grey Zone of strong NDRG-1 diagnosis by Psa expression reinforces the diagnosis of this patient as cancer Primary 1 3.16 Grey Zone of diagnosis by Psa Primary 1 4.87 Grey Zone of diagnosis by Psa Primary 1 4.66 Grey Zone of diagnosis by Psa Primary 1 6.87 Grey Zone of diagnosis by Psa Primary 0 3.91 Grey Zone of diagnosis by Psa Primary 0 6.48 Grey Zone of diagnosis by Psa Primary 2 13.1 possibly cancer Primary 0 4.58 Grey Zone of diagnosis by Psa Primary 1 4.72 Grey Zone of diagnosis by Psa Primary 4 12.71 possibly cancer strong NDRG-1 expression reinforces the diagnosis of this patient as cancer Normal −1 0.8 Normal normal Normal −1 0.8 Normal normal Normal 0 0.6 Normal normal Normal 1 1 Normal normal Normal −1 1.2 Normal normal Normal −1 1.91 Normal normal Normal 2 0.6 Normal normal Normal −1 0.3 Normal normal Normal 0 1 Normal normal Normal −1 0.4 Normal normal Normal −1 0.8 Normal normal Normal 0 1 Normal normal Normal 1 0.8 Normal normal Normal 2 0.6 Normal normal Normal 1 0.5 Normal normal Normal 1 1 Normal normal Normal −1 0.7 Normal normal Normal −1 1.2 Normal normal Normal −1 1.1 Normal normal Normal 0 0.8 Normal normal Normal 0 0.7 Normal normal Normal 0 0.6 Normal normal *scores: no expression, −1; no expression to very faint, 0; expression levels then scored from1 to 6 by intensities

PSA was detected in 100% of the advanced prostate cancers. NDRG1 was detected in 14 out of 18 advanced cancers (78%) (see Table 6, scores greater than 3). Serum PSA levels below 15 ng/ml, particularly, levels between 4-10 ng/ml (often referred to as the ‘grey zone’ in the PSA assay) cannot reliably detect prostate cancer as PSA levels in this range may be the result of other factors such as infection (prostatitis) or benign prostatic hyperplasia (BPH), a common condition in older men. Additionally, the normal range of PSA values increases with patient age. NDRG1 detection in serum reinforced the diagnosis of three prostate cancer patients with PSA levels between 4.9 ng/ml and 15 ng/ml. In these three patients, the NDRG1 scores were 3 or 4, significantly higher than the NDRG1 scores in a cohort of 22 normal individuals (average 0.09, range −1 to 2).

Thus, this example illustrates that the use of two or more prostate specific/enriched cancer markers such as NDRG1 and PSA can improve prostate cancer diagnosis to reduce false positive and false negative rates.

From the foregoing it will be appreciated that, although specific embodiments of the invention have been described herein for purposes of illustration, various modifications may be made without deviating from the spirit and scope of the invention. Accordingly, the invention is not limited except as by the appended claims. 

What is claimed is:
 1. A method for identifying a set of at least 5 organ-specific proteins that are specifically produced in a preselected organ and are shed into the blood comprising, (a) determining, using mRNA transcripts, the sequences of sequences 17-20 nucleotides long that have been identified as signature sequences characteristic of mRNA transcripts that encode proteins from substantially all mRNA transcripts isolated from a sample of said preselected organ; (b) comparing, using a computer, the signature sequences to a database of known RNA transcripts to identify the mRNA transcripts that encode proteins to obtain a preliminary set of mRNA transcripts; (c) comparing, using a computer, the identified mRNA transcripts to mRNA transcripts known to be expressed in other organs; (d) removing, using a computer, from said preliminary set any mRNA transcripts that are known to be substantially expressed in other organs thus obtaining an intermediate set of mRNA transcripts; (e) identifying, using a computer, from the remaining mRNA transcripts in the intermediate set those mRNA transcripts that include a sequence encoding a signal peptide, thereby identifying transcripts that encode organ-specific secreted proteins from said preselected organ; and (f) confirming, using a blood sample, the presence of the organ-specific secreted proteins by assessing the blood sample, thereby identifying a set of at least 5 organ-specific proteins from said preselected organ that are shed into blood.
 2. The method of claim 1 wherein one of said at least 5 organ-specific proteins identified is carboxypeptidase E precursor (CPE) having SEQ ID NO:1520 or olfactory receptor, family 51, subfamily E, member 2 (OR51e2) having SEQ ID NO:1740.
 3. A method for identifying a set of at least 5 organ-specific proteins that are specifically produced in a preselected organ and are shed into the blood comprising, (a) determining, using mRNA transcripts, the sequences of sequences 17-20 nucleotides long that have been identified as signature sequences characteristic of mRNA transcripts that encode proteins from substantially all mRNA transcripts isolated from a sample of said preselected organ; (b) comparing, using a computer, the signature sequences to a database of known RNA transcripts to identify the mRNA transcripts that encode proteins to obtain a preliminary set of mRNA transcripts; (c) identifying, using a computer, from among the mRNA transcripts identified in (b) as encoding proteins, those mRNA transcripts that are expressed in the preselected organ at a level at least 2.5 times the level of expression of the same mRNA transcripts observed in other organs; (d) identifying, using a computer, from the transcripts in (c) those transcripts that include a sequence encoding a signal peptide, thereby identifying transcripts that encode organ-specific proteins secreted or shed into the blood; and (e) confirming, using a blood sample, the presence of the organ-specific secreted proteins by assessing the blood sample thereby identifying a set of at least 5 organ-specific proteins shed into blood.
 4. The method of claim 3 wherein an identified organ-specific protein is carboxypeptidase E precursor (CPE) having SEQ ID NO:1520 or olfactory receptor, family 51, subfamily E, member 2 (OR51e2) having SEQ ID NO:1740. 